Engine power optimization method for hydraulically driven wheeled excavator and excavator thereof

Abstract

The present invention relates to the technical field of wheeled excavator control systems, and in particular to a method for optimizing the engine power of a hydraulically driven wheeled excavator. The method comprises S1, data acquisition; S2, establishing a dynamic utility function: establishing a dynamic utility function based on marginal utility theory; S3, establishing a speed-power mapping relationship: establishing a quadratic function relationship between engine speed and output power. The present invention establishes a parabolic dynamic utility function by applying marginal utility theory, combines the engine output power, the total torque requirement of the hydraulic system, and the engine fuel efficiency, searches for the optimal power point, and simultaneously establishes a quadratic function relationship between the engine speed and output power. The target speed is reversely deduced from the optimal power point, so that the engine is adjusted to the corresponding target speed, thereby effectively saving the operating cost of the wheeled excavator.

Description

Technical Field

[0001] The present invention relates to the technical field of wheel excavator control, and in particular to an engine power optimization control method for a hydraulically driven wheel excavator. Background Art

[0002] Marginal utility refers to the change in utility brought about by each additional unit of goods. Mapping it to engine power control can be understood as follows: in the early operation of the engine, by increasing the power, the torque and fuel efficiency can be improved, and the benefits are increased. However, as the power increases and reaches the power point where the total utility is maximized, the benefits gradually decrease or even become negative. For producers, the power range of the engine should be kept between the positive utility zone and the maximized power point, especially the maximized power point. If the parameters of the various components in the wheeled excavator can be adjusted to keep the engine power at the optimal power point, the operating cost of the wheeled excavator can be effectively saved. Summary of the Invention

[0003] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the structures particularly pointed out in the description and other drawings.

[0004] The purpose of the present invention is to overcome the above shortcomings and provide an engine power optimization control method for a hydraulically driven wheeled excavator. By applying the marginal utility theory to establish a parabolic dynamic utility function, the optimal power point is found by combining the engine output power, the total torque demand of the hydraulic system and the engine fuel efficiency. At the same time, a quadratic function relationship between engine speed and output power is established, and the optimal power point is obtained. The target speed is reversely deduced and the engine is adjusted to the corresponding target speed, thereby effectively saving the operating cost of the wheeled excavator. A weight coefficient is also set in the dynamic utility function. The weight value is dynamically adjusted through Kalman filtering combined with sensor data to adjust the relationship between fuel efficiency and the total torque of the hydraulic system. When the wheeled excavator is climbing a slope or overloaded, the system tolerates fuel efficiency loss in exchange for higher torque.

[0005] The present invention provides an engine power optimization control method for a hydraulically driven wheeled excavator, comprising:

[0006] S1. Data acquisition: A posture sensor is installed in the hydraulically driven wheeled excavator to obtain the excavator's inclination angle in real time. The control center simultaneously collects engine speed, output power, fuel efficiency, and hydraulic system torque data parameters at a sampling frequency of 50 Hz.

[0007] S2. Establish a dynamic utility function: Establish a dynamic utility function based on marginal utility theory :

[0008]

[0009] Where P is the engine output power, T is the total torque requirement of the hydraulic system, is the torque weight, is the growth rate of the hydraulic system torque requirement when the power increases, For engine fuel efficiency, is the efficiency weight, The rate of change of fuel efficiency when power increases is dynamically adjusted through Kalman filtering combined with sensor data and ratio, thereby adjusting the relationship between fuel efficiency and total torque of the hydraulic system and finding the optimal power point , so as to obtain the maximum benefit from the investment in fuel;

[0010] S3. Establishing a speed-power mapping relationship: Establishing a quadratic function relationship between engine speed and output power:

[0011]

[0012] P is the engine output power, N is the engine speed, is the coefficient of the quadratic term of speed, is the first-order coefficient of the speed, is the constant offset, i.e. the engine idle power compensation, 、 、 is the data fitted by the least squares method;

[0013] Based on the optimal power point obtained in step S2 , solve the quadratic function relationship to obtain the target speed under the optimal power. When the hydraulic demand power changes suddenly, the engine speed is adjusted accordingly.

[0014] In some embodiments, in step S2, the calculation formula of the total torque demand of the hydraulic system is:

[0015]

[0016] in, is the pressure of each actuator, is the hydraulic flow of the corresponding actuator, is the mechanical efficiency of the hydraulic motor. When the model of the hydraulic motor is known, the mechanical efficiency of the hydraulic motor is also known.

[0017] In some embodiments, in step S2, if the engine model is known, the engine fuel efficiency As a known quantity, directly substitute it into the dynamic utility function If the engine model is unknown, the engine fuel efficiency The specific calculation formula is:

[0018]

[0019] in, is the current engine output power, is the ECU fuel injection amount, and LHV is the lower heating value of the fuel.

[0020] In some embodiments, in step S2, the optimal power point is found. Dealing with dynamic utility functions However, due to the rapid changes in the power demand of the wheeled excavator during actual operation, a combination of piecewise linearization and iterative search is used to find the optimal power point. , the specific steps are:

[0021] S21, data sampling: get the current T, ;

[0022] S22, local fitting: within the ±5% power range, use a quadratic polynomial to fit curve;

[0023] S23, extreme value search: quickly find the extreme value within the fitting interval through the golden section method The maximum point of

[0024] S24. Output constraint: Limit the result to the safe engine speed range.

[0025] In some embodiments, dynamically adjust and The ratio is achieved by using Kalman filtering and multi-sensor data fusion method:

[0026] First define the state vector :

[0027]

[0028] Observation vector :

[0029]

[0030] in, is the torque weight at time k, is the efficiency weight at time k, is the vehicle body inclination angle obtained by the sensor, is the growth rate of the total torque demand of the hydraulic system when the power increases, is the deviation between the current fuel efficiency and the optimal fuel efficiency, and the state vector and the observation vector Import into the Kalman filter model and get the next time and The weight value of .

[0031] In some embodiments, the Kalman filter model specifically includes:

[0032] Status prediction:

[0033]

[0034] in, is the state prediction value at time k, k-1 is the previous moment, is the identity matrix;

[0035] Covariance prediction:

[0036]

[0037] in, is the covariance prediction value at moment k, k-1 is the previous moment, is the identity matrix, for The transposed matrix of , Q is the process noise covariance, which takes an empirical value;

[0038] Calculate the Kalman gain:

[0039]

[0040] in, is the Kalman gain at time k, is the observation matrix, is the transposed matrix of the observation matrix, R is the measurement noise covariance;

[0041] Status Update :

[0042]

[0043] According to the updated , we can conclude and The specific value of , thus the dynamic utility function Make adjustments;

[0044] Covariance Update :

[0045]

[0046] in, is the identity matrix, and when updating the covariance After that, the values ​​are re-imported into the covariance prediction to perform a new round of Kalman prediction.

[0047] In some embodiments, in step S2, offline calibration is used instead of online calculation to pre-calculate the common working conditions. and The weighted combination reduces the computing load of the controller. When a sensor abnormality occurs, it switches to PID control based on pump pressure to ensure basic operating capabilities.

[0048] In some embodiments, in step S3, the optimal power output based on the marginal utility model is , the specific formula for solving the quadratic equation to obtain the target speed is:

[0049]

[0050] is the target speed, only real number solutions are taken and limited to the safe speed range, which is 1500-2200 rpm.

[0051] The present invention also provides a hydraulically driven wheeled excavator, and applies the engine power optimization control method of the hydraulically driven wheeled excavator.

[0052] By adopting the above technical solution, the beneficial effects of the present invention are:

[0053] The present invention establishes a parabolic dynamic utility function by applying marginal utility theory, and finds the optimal power point by combining engine output power, total torque demand of hydraulic system and engine fuel efficiency. At the same time, a quadratic function relationship between engine speed and output power is established, and the optimal power point is obtained. The target speed is reversely deduced and the engine is adjusted to the corresponding target speed, thereby effectively saving the operating cost of the wheeled excavator. A weight coefficient is also set in the dynamic utility function. The weight value is dynamically adjusted through Kalman filtering combined with sensor data to adjust the relationship between fuel efficiency and the total torque of the hydraulic system. When the wheeled excavator is climbing a slope or overloaded, the system tolerates fuel efficiency loss in exchange for higher torque.

[0054] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0055] Undoubtedly, these and other objects of the present invention will become more apparent after the following detailed description of the preferred embodiment is described with reference to the various figures and drawings.

[0056] In order to make the above and other objects, features and advantages of the present invention more obvious and easy to understand, one or more preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention but do not constitute a limitation of the present invention.

[0058] In the drawings, like components are given like reference numerals, and the drawings are schematic and not necessarily drawn to scale.

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only one or several embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on such drawings without paying any creative work.

[0060] Figure 1 A schematic diagram of the overall process of engine power optimization in some embodiments of the present invention;

[0061] Figure 2 A schematic diagram of a dynamic utility function image under a zero tilt angle in some embodiments of the present invention;

[0062] Figure 3 A schematic diagram of a dynamic utility function image at a 15° tilt angle in some embodiments of the present invention;

[0063] Figure 4 Schematic diagram of the structure of the Kalman filter model in some embodiments of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but are not intended to limit the present invention.

[0065] In addition, in the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0066] In the present invention, unless otherwise expressly specified or limited, terms such as "installed," "connected," "connect," and "fixed" should be interpreted broadly. For example, they may refer to fixed connections, removable connections, or integration; they may refer to direct connections or indirect connections through an intermediate medium; they may refer to internal communication between two components or interactions between two components. However, the term "direct connection" indicates that the two connected entities are not connected through a transitional structure, but are connected solely through a connecting structure to form a single entity. Those skilled in the art will understand the specific meanings of these terms in the present invention based on the specific circumstances.

[0067] In the present invention, unless otherwise clearly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0068] Reference Figure 1 and Figure 4 , Figure 1 A schematic diagram of the overall process of engine power optimization in some embodiments of the present invention; Figure 4 Schematic diagram of the structure of the Kalman filter model in some embodiments of the present invention.

[0069] According to some embodiments of the present invention, the present invention provides an engine power optimization control method for a hydraulically driven wheeled excavator, comprising:

[0070] S1. Data acquisition: A posture sensor is installed in the hydraulically driven wheeled excavator to obtain the excavator's inclination angle in real time. The control center simultaneously collects engine speed, output power, fuel efficiency, and hydraulic system torque data parameters at a sampling frequency of 50 Hz.

[0071] S2. Establish a dynamic utility function: Establish a dynamic utility function based on marginal utility theory :

[0072]

[0073] Where P is the engine output power, T is the total torque requirement of the hydraulic system, is the torque weight, is the growth rate of the hydraulic system torque requirement when the power increases, For engine fuel efficiency, is the efficiency weight, The rate of change of fuel efficiency when power increases is dynamically adjusted through Kalman filtering combined with sensor data and ratio, thereby adjusting the relationship between fuel efficiency and total torque of the hydraulic system and finding the optimal power point , so as to obtain the maximum benefit from the investment in fuel;

[0074] The formula for calculating the total torque demand of the hydraulic system is:

[0075]

[0076] in, is the pressure of each actuator, is the hydraulic flow of the corresponding actuator, is the mechanical efficiency of the hydraulic motor. When the model of the hydraulic motor is known, the mechanical efficiency of the hydraulic motor is also known;

[0077] If the engine model is known, the engine fuel efficiency As a known quantity, directly substitute it into the dynamic utility function If the engine model is unknown, the engine fuel efficiency The specific calculation formula is:

[0078]

[0079] in, is the current engine output power, is the ECU fuel injection amount, LHV is the lower heating value of the fuel;

[0080] Finding the best power point Dealing with dynamic utility functions However, due to the rapid changes in the power demand of the wheeled excavator during actual operation, a combination of piecewise linearization and iterative search is used to find the optimal power point. , the specific steps are:

[0081] S21, data sampling: get the current T, ;

[0082] S22, local fitting: within the ±5% power range, use a quadratic polynomial to fit curve;

[0083] S23, extreme value search: quickly find the extreme value within the fitting interval through the golden section method The maximum point of

[0084] S24, output constraint: limit the result to the safe engine speed range;

[0085] The golden section method approaches the minimum point by continuously narrowing the search interval. Specifically, it selects two points in the search interval and calculates their function values. Based on the size of the function value, it deletes part of the interval so that the remaining interval still contains the minimum point. This process is repeated until the search interval is narrowed to a small enough degree, thereby obtaining an approximate solution to the minimum point. Compared with other search methods, the golden section method can find the maximum or minimum value of the function in a fewer number of iterations and is applicable to various types of unimodal functions, which fits the utility function curve well.

[0086] Dynamic utility function The curve changes are shown in Table 1:

[0087] Table 1

[0088]

[0089] When the controller detects that the current working point deviates When the left side is tilted (such as 1500rpm), the speed is increased to When the peak value is reached, the right deviation (such as 1800rpm) is reduced to avoid entering the negative utility zone, dynamic tracking, and recalculation every 200ms. ;

[0090] Dynamic Adjustment and The ratio is achieved by using Kalman filtering and multi-sensor data fusion method:

[0091] First define the state vector :

[0092]

[0093] Observation vector :

[0094]

[0095] in, is the torque weight at time k, is the efficiency weight at time k, is the vehicle body inclination angle obtained by the sensor, is the growth rate of the total torque demand of the hydraulic system when the power increases, is the deviation between the current fuel efficiency and the optimal fuel efficiency, and the state vector and the observation vector Import into the Kalman filter model and get the next time and The weight value of ;

[0096] The Kalman filter model specifically includes:

[0097] Status prediction:

[0098]

[0099] in, is the state prediction value at time k, k-1 is the previous moment, is the identity matrix;

[0100] Covariance prediction:

[0101]

[0102] in, is the covariance prediction value at moment k, k-1 is the previous moment, is the identity matrix, for The transposed matrix, Q is the process noise covariance, take the empirical value, ;

[0103] Calculate the Kalman gain:

[0104]

[0105] in, is the Kalman gain at time k, is the observation matrix, is the transposed matrix of the observation matrix, R is the measurement noise covariance, , , H and R are both empirical values ​​obtained through historical data;

[0106] Status Update :

[0107]

[0108] According to the updated , we can conclude and The specific value of , thus the dynamic utility function Make adjustments;

[0109] Covariance Update :

[0110]

[0111] in, is the identity matrix, and when updating the covariance After that, the values ​​are re-imported into the covariance prediction to perform a new round of Kalman prediction;

[0112] Dynamic adjustment with an example and The specific process of the ratio:

[0113] When the wheeled excavator is operating on an 8° slope;

[0114] The raw sensor data is: =8.2°±0.5°(noise), =2.8MPa / s, =-1.2%, after Kalman filter correction, =8.0°, =2.7MPa / s, =-1.1%, calculated =1.15, =0.87, which means that the torque capacity is increased by 15% and the fuel efficiency is reduced by 13%. The torque increase is achieved by reducing fuel utilization.

[0115] Under the dual-objective collaborative optimization approach, The torque requirement represents the operating capacity, Fuel efficiency represents economy, and the golden section method can find the optimal power point within a few iterations. Dynamic weighting can be used to achieve a fast-response design of "saving when necessary and boosting when necessary";

[0116] Preferably, offline calibration can be used instead of online calculation, and the and The weighted combination reduces the computing load of the controller. When a sensor abnormality occurs, it switches to PID control based on pump pressure to ensure basic operating capabilities.

[0117] S3. Establishing a speed-power mapping relationship: Establishing a quadratic function relationship between engine speed and output power:

[0118]

[0119] P is the engine output power, N is the engine speed, is the coefficient of the quadratic term of speed, is the first-order coefficient of the speed, is the constant offset, i.e. the engine idle power compensation, 、 、 is the data fitted by the least squares method;

[0120] The process of establishing the speed-power mapping relationship involves engine experiments, namely bench test calibration;

[0121] Take the Cummins QSL9 engine as an example:

[0122] First, establish steady-state calibration points and test three loads at 800 / 1200 / 1400 / 1600 / 2000 / 2200 rpm, respectively, so that the torque reaches 25%, 50% and 75% (maximum torque), respectively. Record the engine output power and use the least squares method to fit the quadratic curve, R 2 Need>0.98;

[0123] After least squares fitting, , , , ; It is understandable that, depending on the actual engine used, 、 、 The specific values ​​are also different and need to be determined through experiments.

[0124] Based on the optimal power point obtained in step S2 , solve the quadratic function relationship to obtain the target speed under the optimal power. When the hydraulic power demand changes suddenly, the engine speed is adjusted accordingly;

[0125] Optimal power output based on marginal utility model , the specific formula for solving the quadratic equation to obtain the target speed is:

[0126]

[0127] is the target speed, only real number solutions are taken and limited to the safe speed range, which is 1500-2200 rpm.

[0128] The present invention also provides a hydraulically driven wheeled excavator, and applies the engine power optimization control method of the hydraulically driven wheeled excavator.

[0129] Example

[0130] Reference Figure 2-3 , Figure 2 A schematic diagram of a dynamic utility function image under a zero tilt angle in some embodiments of the present invention; Figure 3 Schematic diagram of a dynamic utility function image at a 15° inclination angle in some embodiments of the present invention.

[0131] Taking the Cummins QSL9 engine as an example, the basic engine characteristic data is shown in Table 2:

[0132] Table 2

[0133] Speed ​​(rpm) Output power (kW) Fuel efficiency (%) Torque (N·m) 1500 82 38.2 522 1600 92 41.5 550 1700 100 40.8 562 1800 106 39.1 563 1900 110 37.0 553 2000 112 34.5 535

[0134] Utility function calculation example (slope = 0° condition)

[0135] Take the weight coefficient: =0.7, =0.3

[0136] Substituting into the utility function:

[0137]

[0138] Get as Figure 2 The quadratic polynomial fitting utility function of From the curve graph, we can see that:

[0139] The positive utility range is 1500-1610 rpm. Increased power significantly boosts torque, efficiency, and the utility value (U) continues to increase. At the peak of 1610 rpm, the marginal benefit point reaches equilibrium. This point represents the engine's optimal power point, with the lowest fuel consumption rate. Following the optimal power point, the negative utility range begins, with an accelerated rate of efficiency decline, stagnant torque growth, and a negative utility value. The target speed of the peak point, 1610 rpm, is determined by the quadratic function relationship between engine speed and output power.

[0140] Utility function calculation example (slope = 15°)

[0141] The torque demand weight increases at high slopes, and the system tolerates fuel efficiency loss in exchange for higher torque, so it dynamically adjusts and The proportion of From 0.7 to 0.9, From 0.3 to 0.1, due to the influence of the weight coefficient, the peak point shifts to the right, and the controller increases the target speed from 1610 to 1750 rpm;

[0142] like Figure 3 As shown, a double peak phenomenon can be observed in the waveform. This is due to the nonlinear coupling characteristics of the engine and hydraulic system. The peak 1 on the left (N=1610rpm) corresponds to the best fuel economy point, that is, the efficiency peak under low load conditions, which is determined by The main factor is fuel economy. The peak 2 on the right (N=1750rpm) is the result of the attitude sensor detecting an increase in slope and the corresponding adjustment. Adjust the torque weight to increase the torque to form a torque peak, which is exchanged for reducing fuel efficiency. In order to increase the torque, during the actual use of the excavator, a threshold value can be set for the inclination angle. If the attitude sensor directly detects that the current slope is greater than 12°, the second peak of the utility function curve is preferentially selected as the target power point, and the target speed is calculated accordingly.

[0143] It should be understood that the embodiments disclosed herein are not limited to the specific processing steps or materials disclosed herein, but should extend to equivalent substitutions of such features understood by those skilled in the relevant art. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting.

[0144] The "embodiment" mentioned in the specification means that a particular feature or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrase or "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0145] Furthermore, the described features or characteristics may be combined in any other suitable manner into one or more embodiments. In the above description, some specific details, such as thickness, quantity, etc., are provided to provide a comprehensive understanding of the embodiments of the present invention. However, those skilled in the relevant art will appreciate that the present invention may be implemented without one or more of the above specific details or may be implemented using other methods, components, materials, etc.

Claims

1. A method for optimizing engine power control of a hydraulically driven wheeled excavator, characterized in that: include S1. Data acquisition: A posture sensor is installed in the hydraulically driven wheeled excavator to obtain the excavator's inclination angle in real time. The control center simultaneously collects engine speed, output power, fuel efficiency, and hydraulic system torque data parameters at a sampling frequency of 50 Hz. S2. Establish a dynamic utility function: Establish a dynamic utility function based on marginal utility theory : Where P is the engine output power, T is the total torque requirement of the hydraulic system, is the torque weight, is the growth rate of the hydraulic system torque requirement when the power increases, For engine fuel efficiency, is the efficiency weight, The rate of change of fuel efficiency when power increases is dynamically adjusted through Kalman filtering combined with sensor data and ratio, thereby adjusting the relationship between fuel efficiency and total torque of the hydraulic system and finding the optimal power point , so as to obtain the maximum benefit from the investment in fuel; S3. Establishing a speed-power mapping relationship: Establishing a quadratic function relationship between engine speed and output power: P is the engine output power, N is the engine speed, is the coefficient of the quadratic term of speed, is the first-order coefficient of the speed, is the constant offset, i.e. the engine idle power compensation, 、 、 is the data fitted by the least squares method; Based on the optimal power point obtained in step S2 , solve the quadratic function relationship to obtain the target speed under the optimal power. When the hydraulic demand power changes suddenly, the engine speed is adjusted accordingly.

2. The engine power optimization control method for a hydraulically driven wheeled excavator according to claim 1, characterized in that: In step S2, the total torque requirement of the hydraulic system is calculated as: in, is the pressure of each actuator, is the hydraulic flow of the corresponding actuator, is the mechanical efficiency of the hydraulic motor. When the model of the hydraulic motor is known, the mechanical efficiency of the hydraulic motor is also known.

3. The engine power optimization control method for a hydraulically driven wheeled excavator according to claim 2, characterized in that: In step S2, if the engine model is known, the engine fuel efficiency As a known quantity, directly substitute it into the dynamic utility function If the engine model is unknown, the engine fuel efficiency The specific calculation formula is: in, is the current engine output power, is the ECU fuel injection amount, and LHV is the lower heating value of the fuel.

4. The engine power optimization control method for a hydraulically driven wheeled excavator according to claim 3, characterized in that: In step S2, find the optimal power point Dealing with dynamic utility functions However, due to the rapid changes in the power demand of the wheeled excavator during actual operation, a combination of piecewise linearization and iterative search is used to quickly find the optimal power point. , the specific steps are: S21, data sampling: get the current T, ; S22, local fitting: within the ±5% power range, use a quadratic polynomial to fit curve; S23, extreme value search: quickly find the extreme value within the fitting interval through the golden section method The maximum point of S24. Output constraint: Limit the result to the safe engine speed range.

5. The engine power optimization control method for a hydraulically driven wheeled excavator according to claim 1, characterized in that: Dynamic Adjustment and The ratio is achieved by using Kalman filtering and multi-sensor data fusion method: First define the state vector : Observation vector : in, is the torque weight at time k, is the efficiency weight at time k, is the vehicle body inclination angle obtained by the sensor, is the growth rate of the total torque demand of the hydraulic system when the power increases, is the deviation between the current fuel efficiency and the optimal fuel efficiency, and the state vector and the observation vector Import into the Kalman filter model and get the next time and The weight value of .

6. The engine power optimization control method for a hydraulically driven wheeled excavator according to claim 5, characterized in that: The Kalman filter model specifically includes: Status prediction: in, is the state prediction value at time k, k-1 is the previous moment, is the identity matrix; Covariance prediction: in, is the covariance prediction value at moment k, k-1 is the previous moment, is the identity matrix, for The transposed matrix of , Q is the process noise covariance, which takes an empirical value; Calculate the Kalman gain: in, is the Kalman gain at time k, is the observation matrix, is the transposed matrix of the observation matrix, R is the measurement noise covariance; Status Update : According to the updated , we can conclude and The specific value of , thus the dynamic utility function Make adjustments; Covariance Update : in, is the identity matrix, and when updating the covariance After that, the values ​​are re-imported into the covariance prediction to perform a new round of Kalman prediction.

7. The engine power optimization control method for a hydraulically driven wheeled excavator according to claim 6, characterized in that: In step S2, offline calibration is used instead of online calculation to pre-calculate the and The weighted combination reduces the computing load of the controller. When a sensor abnormality occurs, it switches to PID control based on pump pressure to ensure basic operating capabilities.

8. The engine power optimization control method for a hydraulically driven wheeled excavator according to claim 1, characterized in that: In step S3, the optimal power output based on the marginal utility model is , the specific formula for solving the quadratic equation to obtain the target speed is: is the target speed, only real number solutions are taken and limited to the safe speed range, which is 1500-2200 rpm.

9. A hydraulically driven wheel excavator, characterized in that: An engine power optimization control method for a hydraulically driven wheeled excavator according to any one of claims 1 to 8 is applied.

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