Hybrid energy management equivalent factor self-adaption method for heavy-duty commercial combination vehicle

Abstract

The invention relates to a heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method, which comprises the steps of classifying operation conditions based on cloud historical data of the operation conditions, and calculating an optimal equivalent factor under each operation condition in an off-line manner by utilizing simulation software after classification is completed; selecting a working condition updating period taking mileage as a reference, identifying the working condition according to the characteristic parameters in the period, and obtaining a corresponding optimal equivalent factor as a reference equivalent factor of the next control period; the equivalent factors are adjusted in real time based on the whole vehicle state parameters; adjusting equivalent factors considering terrain prediction according to the difference between the allowable maximum SOC before downhill and the current SOC in combination with front terrain prediction; corresponding control instructions are sent to the motor and the engine; periodically updating the reference equivalent factor to realize continuous loop iteration of the optimal equivalent factor; and the fuel economy of the whole vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of commercial vehicles, and in particular to an adaptive method for equivalent factor of hybrid energy management of heavy-duty commercial vehicle trains. Background Art

[0002] In recent years, with the explosive development of passenger car electrification, the scale effect of related industries such as power batteries and automotive motors has become increasingly significant, providing support for the electrification of commercial vehicles. However, due to the higher total mass and longer transportation distance requirements of heavy-duty commercial vehicles compared to passenger vehicles, hybrid power will still serve as the main technical route for the new energy of heavy-duty commercial vehicles in the next 5 to 10 years.

[0003] Taking into account the complex and changeable operating scenarios of heavy-duty commercial vehicles, on the basis of limited power configuration, how to carry out adaptive adjustment of energy management algorithms according to changes in operating conditions such as load status (empty, fully loaded), road terrain (plains, mountainous areas, hills) and road grade (highways, national highways) of heavy-duty commercial vehicles to achieve stable fuel economy performance is a core vehicle control issue that needs to be urgently solved for heavy-duty hybrid commercial vehicles.

[0004] At present, the most widely used hybrid energy management technology is the ECMS strategy. Its basic principle is to convert the power consumption into the fuel consumed by the future engine to produce the same amount of electricity through a preset equivalent factor, so as to obtain a corresponding equivalent fuel consumption and actual engine fuel consumption for different power output schemes, and select the power configuration scheme with the smallest sum of the two as the control quantity output, so as to better balance the power consumption and fuel consumption and achieve good fuel economy. In this algorithm, the equivalent factor has a direct and significant impact on the power allocation scheme and the final fuel saving effect. If the value of the equivalent factor is too large, the control strategy will tend to call the engine power, resulting in increased fuel consumption, and the electric drive system cannot fully play the role of peak shaving and valley filling; if the value of the equivalent factor is too small, the control strategy will tend to call the motor power, resulting in excessive power consumption and difficulty in balancing the SOC. The current control strategy is mostly to adaptively adjust the equivalent factor according to the SOC value, which can only passively ensure the balance of the SOC and cannot achieve a higher fuel saving rate. Summary of the invention

[0005] The purpose of the present invention is to provide a method for adaptively controlling the equivalent factor of hybrid energy management of heavy-duty commercial vehicle trains in view of the deficiencies in the prior art.

[0006] The present invention is achieved by adopting the following technical solutions: A heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method, comprising: S1. Data preparation: Based on the historical cloud data of the operating conditions, classify the operating conditions according to the characteristic parameters of average vehicle speed, acceleration, deceleration, constant speed, idling ratio, average acceleration, average deceleration, average slope and slope variance. After the classification is completed, use simulation software to calculate the optimal equivalent factor under each operating condition offline to obtain the corresponding table of operating condition - equivalent factor. At the same time, calculate according to the acceleration and driving force during the pure - electric starting process to obtain the vehicle total mass information; S2. Online operating condition identification and periodic adjustment of equivalent factor: According to the operating conditions, select the operating condition update cycle based on mileage. Identify the operating condition to which it belongs according to the characteristic parameters within the cycle, and obtain the corresponding optimal equivalent factor as the reference equivalent factor for the next control cycle; S3. Real - time adjustment of equivalent factor based on vehicle state parameters: According to the current SOC and allowable charge - discharge current, construct the vehicle state parameter penalty function f v , and make real - time adjustment to the reference equivalent factor to obtain the real - time adjusted equivalent factor; S4. Based on the prediction of the terrain ahead, obtain the slope and slope length information to get the predictive map information. According to the current vehicle total mass and motor recovery power, convert to obtain the recoverable electric energy during the downhill process, and calculate the maximum SOC before downhill according to the recoverable electric energy. According to the difference between the maximum SOC before downhill and the current SOC, make a further adjustment to the equivalent factor considering terrain prediction; S5. Determine the adaptive equivalent factor based on the reference equivalent factor in S2, the real - time adjusted equivalent factor in S3, and the equivalent factor considering terrain prediction in S4, calculate the optimal power - torque distribution scheme between the engine and the motor, and send corresponding control commands to the motor and the engine; S6. The characteristic parameter identification module updates the characteristic parameters of the historical data in real - time, and periodically updates the reference equivalent factor according to the update cycle to realize the continuous cyclic iteration of the optimal equivalent factor.

[0007] Preferably, in S4, the adjustment strategy of the equivalent factor considering terrain prediction is: According to the difference between the maximum SOC before downhill and the current SOC, if the current SOC value is greater than the maximum SOC before downhill, then reduce the equivalent factor; if the current SOC value is less than the maximum SOC before downhill, then do not adjust; and construct a penalty function f with a value between 0.5 and 1 according to the adjustment strategy p .

[0008] Preferably, in S1, after completing the classification of operating conditions and calculating the optimal equivalent factor, new historical driving data is selected to verify the classification algorithm and the optimal equivalent factor; when calculating the total vehicle mass, the data during the gear shifting process during pure electric start of the vehicle is blocked, and at the same time, according to the feedback signal of the on-vehicle slope sensor, the influence of the slope start process on the acceleration is avoided to ensure accurate calculation of the total vehicle mass; the operating conditions include plain, mountainous area and urban area.

[0009] Preferably, in S2, the determination of the recognition period needs to consider the operating condition characteristics, which include engineering vehicles with frequent operating condition changes and highway vehicles with relatively stable operating conditions. For engineering vehicles with frequent operating condition changes, a recognition-update period of 3 - 5 km is selected; for highway vehicles with relatively stable operating conditions, a recognition-update period of 30 - 50 km is selected.

[0010] Preferably, in S3, the vehicle state parameters include the SOC value of the power battery, as well as the maximum charging current and discharging current allowed by the power battery. When the current is limited, the reference equivalent factor is increased to avoid over-discharging of the power battery.

[0011] Preferably, in S4, the prediction of the terrain ahead, where the terrain ahead includes suburban roads, national roads, and highway roads; for engineering vehicles with suburban roads or national roads as the operating scenario ahead, the prediction distance is 10 km, and for highway vehicles with highway roads as the operating scenario of the current terrain, the prediction distance is 20 km.

[0012] Preferably, when the torque distribution scheme is executed in S5, due to the difference in the response speeds of the engine and the motor, to ensure that the wheel torque output always remains consistent with the driver's required torque mapped by the throttle opening, the engine required torque calculated by the energy management module is sent as a control message instruction to the engine controller, and the difference between the engine required torque and the actual engine torque is sent as a motor control instruction to the motor controller.

[0013] Preferably, in S3, the vehicle state parameter penalty function f v is obtained by multiplying the SOC penalty function f1 and the allowable charge and discharge current penalty function f2; The constructed SOC penalty function f1 is as follows: ; where DEVsoc is the difference between the current SOC value and the SOC median value of 0.5; f1( t ) is the value of the SOC penalty function f1 at t moment; According to the allowable charge and discharge current penalty function f2, a function is constructed: ; In the formulaPer I (t) is the larger value of the ratio of the currently allowed charging current to the maximum charging current of the power battery and the ratio of the currently allowed discharging current to the maximum discharging current of the power battery, and f2(t) is t the value of the charge and discharge current penalty function f2 at the moment, which is Per I a cubic function of (t).

[0014] Preferably, in S4, the equivalent factor considering terrain prediction is adjusted to identify the length and slope value of the negative slope according to the slope of the road ahead, and obtain the recoverable electric quantity based on the estimated downhill vehicle speed: ; A negative slope value refers to a downhill section, and the length of the slope value being negative is the length of the downhill section; In the formula, G is the total vehicle mass, f is the rolling resistance coefficient, C D is the wind resistance coefficient, A is the frontal projected area of the vehicle, a is the slope, U a is the vehicle speed, L is the slope length, mot is the generator efficiency, bat is the battery charging power; Then calculate the upper limit value of SOC when the vehicle is at the top of the slope SOC limt : ; According to the recoverable electric quantity Q and the battery capacity Q bat the ratio of, obtain the required SOC capacity, and further obtain SOC limt the upper limit value; when there is no negative slope within the foreseeable range, SOC limt the upper limit value is processed as 100%.

[0015] Preferably, to avoid SOC being higher than SOC limt the upper limit value when the vehicle reaches the top of the slope, it is necessary to adaptively adjust the equivalent factor: ; When SOC is less than SOC limt the upper limit value, no further adjustment is made to the equivalent factor, and the penalty function f p = 1; when SOC is greater thanSOC limt When the upper limit value is reached, and the difference is large and it is close to the top of the slope, the penalty function f p takes a value less than 1 and adjusts the power to SOC limt within the upper limit value; The penalty function f p specifically takes into account the power difference SOC - SOC limit and the distance L from the top of the slope peak and uses a two-dimensional look-up table method for value selection. The closer the distance L from the top of the slope peak , the greater the adjustment amplitude. The greater the difference between SOC and SOC limt the upper limit value, the greater the adjustment; The finally constructed adaptive equivalent factor adjustment function is: .

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention can adjust the equivalent factor in a combined manner of periodic adjustment and real-time adjustment by comprehensively considering the operating conditions, vehicle state parameters, and the terrain ahead, improving the adaptability of the vehicle's energy management program and improving the fuel economy performance of the vehicle. Brief Description of the Drawings

[0017] Figure 1 is a flowchart of the present invention; Figure 2 is a schematic diagram of the SOC penalty function f1 of the present invention; Figure 3 is a schematic diagram of the charge and discharge current penalty function f2 of the present invention. Detailed Embodiment

[0018] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] As Figure 1 shown, a method for adapting the equivalent factor of the hybrid energy management of a heavy commercial vehicle train includes: S1. Data preparation: Based on the historical cloud data of the operating conditions, classify the operating conditions according to the characteristic parameters of average vehicle speed, acceleration, deceleration, constant speed, idling ratio, average acceleration, average deceleration, average slope and slope variance. After classification, use simulation software to calculate the optimal equivalent factor under each operating condition offline to obtain the corresponding table of operating condition - equivalent factor. At the same time, calculate according to the acceleration and driving force during the pure - electric starting process to obtain the vehicle total mass information; S2. Online operating condition identification and periodic adjustment of equivalent factor: According to the operating conditions, select the operating condition update period based on mileage. Identify the operating condition to which it belongs according to the characteristic parameters within the period, and obtain the corresponding optimal equivalent factor as the reference equivalent factor for the next control period; S3. Real - time adjustment of equivalent factor based on vehicle state parameters: According to the current SOC and allowable charge - discharge current, construct the vehicle state parameter penalty function f v , and make real - time adjustment to the reference equivalent factor to obtain the real - time adjusted equivalent factor; S4. Based on the prediction of the terrain ahead, obtain the slope and slope length information to get the predictive map information. According to the current vehicle total mass and motor recovery power, convert to obtain the recoverable power during the downhill process, and calculate the maximum SOC before downhill according to the recoverable power. According to the difference between the maximum SOC before downhill and the current SOC, make a further adjustment to the equivalent factor considering terrain prediction; S5. Determine the adaptive equivalent factor based on the reference equivalent factor in S2, the real - time adjusted equivalent factor in S3, and the equivalent factor considering terrain prediction in S4, calculate the optimal power - torque distribution scheme between the engine and the motor, and send corresponding control commands to the motor and the engine; S6. The characteristic parameter identification module updates the characteristic parameters of the historical data in real - time, and periodically updates the reference equivalent factor according to the update period to realize the continuous cyclic iteration of the optimal equivalent factor.

[0020] In S4, the adjustment strategy of the equivalent factor considering terrain prediction is: According to the difference between the maximum SOC before downhill and the current SOC, if the current SOC value is greater than the maximum SOC before downhill, then reduce the equivalent factor; if the current SOC value is less than the maximum SOC before downhill, then do not adjust; and construct a penalty function f with a value between 0.5 and 1 according to the adjustment strategy p .

[0021] In S1, after completing the working condition classification and calculating the optimal equivalent factor, new historical driving data is selected to verify the classification algorithm and the optimal equivalent factor. When calculating the total vehicle mass, the data during the gear shifting process at the start of pure electric driving of the vehicle is blocked, and at the same time, according to the feedback signal of the on-vehicle slope sensor, the influence of the ramp start process on the acceleration is avoided to ensure accurate calculation of the total vehicle mass. The operating conditions include plain, mountainous area and urban area.

[0022] In S2, the determination of the identification period needs to consider the operating condition characteristics, which include engineering vehicles with frequent working condition changes and highway vehicles with relatively stable working conditions. For engineering vehicles with frequent working condition changes, a working condition identification-update period of 3 - 5 km is selected; for highway vehicles with relatively stable working conditions, a working condition identification-update period of 30 - 50 km is selected.

[0023] In S3, the vehicle state parameters include the SOC value of the power battery, as well as the maximum charging current and discharging current allowed by the power battery. When the current is limited, the reference equivalent factor is increased to avoid over-discharging of the power battery.

[0024] In S4, the prediction of the terrain ahead, where the terrain ahead includes suburban roads, national roads, and highway roads; for engineering vehicles with suburban roads or national roads as the operating scenario ahead, the prediction distance is 10 km, and for highway vehicles with highway roads as the operating scenario of the current terrain, the prediction distance is 20 km.

[0025] In S5, when the torque distribution scheme is executed, due to the difference in the response speeds of the engine and the motor, to ensure that the wheel-side torque output always remains consistent with the driver's required torque mapped by the throttle opening, the engine required torque calculated by the energy management module is sent as a control message instruction to the engine controller, and the difference between the engine required torque and the actual engine torque is sent as a motor control instruction to the motor controller.

[0026] In S3, the vehicle state parameter penalty function f v is obtained by multiplying the SOC penalty function f1 and the allowable charge and discharge current penalty function f2; The schematic diagram of the piecewise function constructed according to the SOC penalty function f1 is as Figure 2 shown: ; where DEVsoc is the difference between the current SOC value and the SOC median value of 0.5; f1( t ) is t the value of the SOC penalty function f1 at The schematic diagram of the piecewise function constructed according to the allowable charge and discharge current penalty function f2 is as Figure 3 shown: ; In the formula Per I (t) is the larger value of the ratio of the currently allowed charging current to the maximum charging current of the power battery and the ratio of the currently allowed discharging current to the maximum discharging current of the power battery, and f2(t) is t the value of the charge and discharge current penalty function f2 at time t, and is Per I a cubic function of (t).

[0027] In S4, the equivalent factor considering terrain prediction is adjusted as follows: according to the slope of the road ahead, identify the length and slope value of the negative slope, and obtain the recoverable electric energy based on the estimated downhill vehicle speed: ; A negative slope value indicates a downhill section, and the length of the slope value being negative is the length of the downhill section; In the formula, G is the total vehicle mass, f is the rolling resistance coefficient, C D is the wind resistance coefficient, A is the vehicle's frontal projected area, a is the slope, U a is the vehicle speed, L is the slope length, mot is the generator efficiency, bat is the battery charging power; Then calculate the upper limit value of SOC when the vehicle is at the top of the slope SOC limt : ; According to the ratio of the recoverable electric energy Q to the battery capacity Q bat , obtain the required SOC capacity, and further obtain SOC limt the upper limit value; when there is no negative slope within the foreseeable range, SOC limt the upper limit value is processed as 100%.

[0028] To prevent SOC from being higher than SOC limt the upper limit value when the vehicle reaches the top of the slope, it is necessary to adaptively adjust the equivalent factor: ; When SOC is less than SOC limt the upper limit value, no further adjustment is made to the equivalent factor, and the penalty function f p= 1; When the SOC is greater than SOC limt the upper limit value, and the difference is large and it is close to the top of the slope, the penalty function f p takes a value less than 1, and adjusts the power to SOC limt within the upper limit value; The specific value of the penalty function f p considers the power difference SOC - SOC limit and the distance L from the top of the slope peak , and the value is obtained by using the two-dimensional look-up table method. The closer the distance L from the top of the slope peak is, the greater the adjustment amplitude. The greater the difference between the SOC and SOC limt the upper limit value, the greater the adjustment; The two-dimensional look-up table data is as follows: ; The finally constructed adaptive equivalent factor adjustment function is: .

Claims

1. A heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method, characterized in that: include: S1, data preparation, based on the cloud historical data of operating conditions, the operating conditions are classified according to the characteristic parameters of average vehicle speed, acceleration, deceleration, constant speed, idle ratio, average acceleration, average deceleration, average slope and slope variance. After the classification is completed, the optimal equivalent factor under each operating condition is calculated offline using simulation software to obtain the operating condition-equivalent factor correspondence table; at the same time, the acceleration and driving force of the pure electric starting process are calculated to obtain the total mass information of the vehicle; S2, online working condition identification and periodic adjustment of equivalent factors. According to the operating conditions, the working condition update cycle based on mileage is selected, the working condition is identified according to the characteristic parameters within the cycle, and the corresponding optimal equivalent factor is obtained as the benchmark equivalent factor for the next control cycle; S3, based on the real-time adjustment of the equivalent factor of the vehicle state parameters, constructs the vehicle state parameter penalty function f according to the current SOC and the allowed charge and discharge current v , adjust the benchmark equivalent factor in real time to obtain the equivalent factor adjusted in real time; S4, based on the terrain prediction ahead, obtain the slope and slope length information, obtain the predictive map information, convert the recoverable electricity in the downhill process according to the current vehicle gross mass and the motor recovery power, and calculate the maximum SOC before the downhill according to the recoverable electricity, and further adjust the equivalent factor considering the terrain prediction according to the difference between the maximum SOC before the downhill and the current SOC; S5, determining an adaptive equivalent factor based on the reference equivalent factor in S2, the equivalent factor adjusted in real time in S3, and the equivalent factor considering terrain prediction in S4, calculating the optimal power torque distribution scheme between the engine and the motor, and sending corresponding control instructions to the motor and the engine; S6, the characteristic parameter identification module updates the characteristic parameters of the historical data in real time, and periodically updates the benchmark equivalent factor according to the update cycle to achieve continuous cyclic iteration of the optimal equivalent factor.

2. The heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method according to claim 1 is characterized in that: In S4, the adjustment strategy of the equivalent factor considering terrain prediction is: according to the difference between the maximum SOC before downhill and the current SOC, if the current SOC value is greater than the maximum SOC value before downhill, the equivalent factor is reduced; if the current SOC value is less than the maximum SOC value before downhill, no adjustment is made; and a penalty function f with a value between 0.5 and 1 is constructed based on the adjustment strategy. p .

3. The adaptive method for equivalent factor of hybrid energy management of heavy commercial vehicle train according to claim 1 is characterized in that: In S1, after completing the classification of the operating conditions and the calculation of their optimal equivalent factors, new historical driving data is selected to verify the classification algorithm and the optimal equivalent factor; when calculating the total mass of the vehicle, the data during the gear shifting process when the vehicle starts in pure electric mode is shielded, and at the same time, based on the feedback signal of the on-board slope sensor, the influence of the slope starting process on the acceleration is avoided, resulting in the inability to accurately calculate the total mass of the vehicle; the operating conditions include plains, mountainous areas and urban areas.

4. The heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method according to claim 1 is characterized in that: The determination of the identification cycle in S2 needs to take into account the operating condition characteristics, which include engineering vehicles with frequently changing operating conditions and road vehicles with relatively stable operating conditions. For engineering vehicles with frequently changing operating conditions, a 3-5km operating condition identification-update cycle is selected; For road vehicles with relatively stable operating conditions, a 30-50km operating condition identification-update cycle is selected.

5. The self-adaptive method for equivalent factor of hybrid energy management of heavy-duty commercial vehicle train according to claim 1 is characterized in that: The vehicle status parameters in S3 include the SOC value of the power battery and the maximum charging current and discharging current allowed by the power battery. When the current is limited, the benchmark equivalent factor is increased to avoid over-discharge of the power battery.

6. The heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method according to claim 2 is characterized in that: S4 predicts the terrain ahead, where the terrain ahead includes suburban roads, national highways, and expressways. When the current terrain ahead is a suburban road or national highway for an engineering vehicle, the predicted distance is 10km; when the current terrain ahead is a expressway for a road vehicle, the predicted distance is 20km.

7. The self-adaptive method for equivalent factor of hybrid energy management of heavy-duty commercial vehicle train according to claim 1 is characterized in that: When the torque distribution scheme in S5 is executed, due to the difference in response speed between the engine and the motor, in order to ensure that the wheel-side torque output always remains consistent with the driver's required torque mapped by the throttle opening, the engine required torque calculated by the energy management module is sent to the engine controller as a control message instruction, and the difference between the engine required torque and the actual engine torque is sent to the motor controller as a motor control instruction.

8. The self-adaptive method for equivalent factor of hybrid energy management of heavy-duty commercial vehicle train according to claim 5 is characterized in that: In S3, the vehicle state parameter penalty function f v It is obtained by multiplying the SOC penalty function f1 and the allowed charge and discharge current penalty function f2; Construct a piecewise function based on the SOC penalty function f1: ； in DEVsoc is the difference between the current SOC value and the SOC median value of 0.5; f1( t )for t The value of the SOC penalty function f1 at the moment; Construct the function based on the allowed charge and discharge current penalty function f2: ； In the formula Per I (t) is the larger value of the ratio of the current allowed charging current to the maximum charging current of the power battery and the ratio of the current allowed discharging current to the maximum discharging current of the power battery, and f2(t) is t The value of the charge and discharge current penalty function f2 at the moment is Per I (t) is a cubic function of 9. The self-adaptive method for equivalent factor of hybrid energy management of heavy commercial vehicle train according to claim 2, characterized in that: The equivalent factor adjustment considering terrain prediction in S4 is to identify the length and slope value of the negative slope according to the slope of the road ahead, and obtain the recoverable power according to the estimated downhill speed: ； A negative slope refers to a downhill section, and the length of a negative slope is the length of the downhill section; In the formula, G is the total mass of the vehicle, f is the rolling resistance coefficient, C D is the drag coefficient, A is the vehicle's orthographic projection area, α For slope, U a is the vehicle speed, L is the slope length, mot is the generator efficiency, bat Charging power for batteries; Then calculate the SOC upper limit when the vehicle is at the top of the slope SOC limt : ； According to the recoverable amount Q With battery capacity Q bat The ratio of , get the required SOC capacity, and further get SOC limt Upper limit value; when there is no negative slope value within the foreseeable range, SOC limt The upper limit value is treated as 100%.

10. The heavy-duty commercial vehicle train hybrid energy management equivalent factor adaptive method according to claim 9, characterized in that: To prevent the SOC from exceeding SOC limt Upper limit value, the equivalent factor needs to be adaptively adjusted: ； When SOC is less than SOC limt When the upper limit is reached, no further adjustment is made to the equivalent factor, and the penalty function f p =1; when SOC is greater than SOC limt When the upper limit value is large and the difference is close to the top of the slope, the penalty function f p Take a value less than 1 and adjust the power to SOC limt Within the upper limit value; Penalty function f p The specific value takes into account the power difference SOC-SOC limit and the distance L from the top of the slope peak , the distance L from the top of the slope is obtained by using a two-dimensional table lookup method. peak The closer, the greater the adjustment, SOC and SOC limt The greater the difference in the upper limit values, the greater the adjustment; The final adaptive equivalent factor adjustment function is: 。

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