A method for collaborative optimization of working parameters of a wheel loader

Abstract

The application provides a wheel loader working parameter cooperative optimization method, first divides the loader running distance step by step, ensures that each step distance is equal, then adopts a dynamic programming-Brent combination algorithm, combines an initial weight factor, accurately calculates a single step cost function and a step time length in each step state, and accumulatively calculates a total running time length of the loader. If there is a gap with a preset time length, a root algorithm is used to dynamically adjust the weight factor until the preset condition is met. This process realizes cooperative optimization of the driving speed and the energy management strategy, effectively reduces the battery charging and discharging rate and frequency, and slows down the battery aging. Compared with the existing electro-hydraulic parallel hybrid drive system, the application not only significantly improves the energy utilization efficiency, but also greatly prolongs the service life of the battery, and improves the overall economy and environmental protection of the loader.

Description

Technical Field

[0001] This application relates to the field of engineering machinery technology, and in particular to a method for collaborative optimization of working parameters of a wheel loader. Background Technology

[0002] Wheel loaders, as important engineering construction equipment, are widely used in national defense, coal mining, and other operational scenarios. Traditional wheel loaders are driven by internal combustion engines, resulting in high energy consumption and significant carbon emissions. Electrification is a crucial technological approach to achieving zero emissions and high-efficiency operation for wheel loaders. Wheel loaders operate in a limited range of scenarios, with typical driving conditions resembling a V-shape. Due to the heavy load and frequent heavy-load starts and stops, wheel loaders experience high battery charge / discharge rates and frequent charging / discharging, leading to short battery life.

[0003] To extend battery life, current solutions primarily employ an electro-hydraulic parallel hybrid drive system. This system uses a hydraulic system to recover braking energy and, during loader acceleration, works in conjunction with the electric system to drive the vehicle. This reduces battery charging and discharging current and decreases battery usage frequency, thus extending battery life. However, controlling an electro-hydraulic parallel hybrid drive system is challenging, and the optimal power allocation between the electric and hydraulic systems remains a research hurdle. Furthermore, current research has not optimized driving speeds for electro-hydraulic parallel hybrid drive systems, leaving significant room for improvement in battery life. Summary of the Invention

[0004] The purpose of this application is to provide a collaborative optimization method for the working parameters of a wheel loader, which can improve the above-mentioned problems.

[0005] The embodiments of this application are implemented as follows:

[0006] This application provides a method for collaborative optimization of the operating parameters of a wheel loader, used to optimize the operating parameters of a wheel loader employing an electro-hydraulic parallel hybrid power system, comprising steps S1 to S5. Here, S1, S2, etc., are merely step identifiers, and the execution order of the method does not necessarily follow an ascending numerical order; for example, step S2 may be executed before step S1, and this application does not impose any restrictions.

[0007] S1, the overall running distance of the wheel loader is divided into steps, with each step being equal in distance;

[0008] S2, use the dynamic programming-Brent combinatorial algorithm to calculate the single-step cost function for each step state, substitute the initial weight factor, and calculate the corresponding single-step runtime based on the cost function;

[0009] S3, sum up the duration of each single step during which the wheel loader completes the overall running distance to obtain the total running time;

[0010] S4. If the difference between the total runtime and the preset runtime is greater than the preset allowable runtime error, the weight factor is updated by the root-finding algorithm, and the dynamic programming-Brenter combination algorithm is re-executed until the runtime difference is less than the preset allowable runtime error.

[0011] S5, obtain the optimal single-step cost function corresponding to the time difference being less than the preset allowable time error, extract the control variable corresponding to the optimal single-step cost function to output the optimal control parameter, output the optimal control parameter curve composed of the optimal control parameters corresponding to each step state, extract the state variable corresponding to the optimal single-step cost function to output the optimal driving speed, and output the optimal driving speed curve composed of the optimal driving speed corresponding to each step state.

[0012] It is understandable that this application has achieved significant beneficial effects in extending the battery life of wheel loaders. On the one hand, by optimizing the overall machine operating cost, which consists of power consumption and battery aging, the application effectively reduces the overall operating cost of the loader by co-optimizing the driving speed and energy management strategy of the electro-hydraulic parallel hybrid drive system. This reduces both power consumption and slows down battery aging. On the other hand, an innovative solution method for the driving speed-power distribution co-optimization problem is designed. This method can efficiently and accurately solve for the optimal parameters, improving the efficiency and accuracy of the optimization process.

[0013] In an optional embodiment of this application, the step of using the dynamic programming-Brent combinatorial algorithm to calculate the single-step cost function for each step state includes:

[0014] The sequence of state variables is determined according to the following formula: ;

[0015] in, Representing the A sequence of state variables under each step state. This represents the travel speed of the wheel loader. Represents the energy state of the hydraulic accumulator;

[0016] The sequence of control variables is determined according to the following formula: ;

[0017] in, Representing the The sequence of control variables in each step state Represents the motor drive torque. Represents the driving torque of the hydraulic pump / motor;

[0018] Following the sequence from the end point of the wheel loader's operation to the starting point of the wheel loader's operation, and based on the boundary conditions of the wheel loader's system parameters, the single-step cost function for each step state is calculated sequentially using the following formula: ;

[0019] in, Representing the Single-step cost function under each step state Representing the The step state is up to the first The comprehensive cost function for each step state. Representing the The step state is up to the first The comprehensive cost function for each step state.

[0020] In an optional embodiment of this application, the first The single-step cost function for each step state is as follows: ;

[0021] in, This represents the initial state variable sequence.

[0022] In optional embodiments of this application, the boundary conditions for the system parameters of the wheel loader include at least one of the following:

[0023] No. The sequence of state variables under each step state The travel speed of the wheel loader mentioned in the text Belongs to the A preset driving speed threshold range ,Right now ;

[0024] No. The sequence of state variables under each step state The energy state of the hydraulic accumulator mentioned in Belongs to the A preset hydraulic accumulator energy threshold range ,Right now ;

[0025] No. Control variable sequence in each step state The motor drive torque mentioned in Within the preset motor drive torque threshold range ,Right now ;

[0026] No. Control variable sequence in each step state The hydraulic pump / motor drive torque in Within the preset hydraulic pump / motor drive torque threshold range ,Right now .

[0027] In an optional embodiment of this application, the comprehensive cost function is constructed based on the system parameters, state variables, and control variables of the wheel loader, and includes the runtime and a weighting factor for allocating and optimizing the cost value and runtime of the comprehensive cost function. The comprehensive cost function is as follows: ;

[0028] in, The comprehensive cost function represents the wheel loader. This represents the current travel distance of the wheel loader. This represents the final distance traveled by the wheel loader at its destination. Represents electricity price, Represents the battery output power. Represents battery price, Represents the charge / discharge intensity coefficient. Represents battery capacity. This represents the total number of battery discharge cycles under nominal operating conditions. Represents the battery's output current. This represents the travel speed of the wheel loader, the travel speed With runtime Related: , This represents the weighting factors used to allocate the cost value and runtime of the comprehensive cost function for optimization.

[0029] In an optional embodiment of this application, the driving speed It also satisfies the following formula: ;

[0030] in, Represents the gearbox transmission ratio. This represents the final drive ratio. This represents the efficiency of the transmission system. Represents the motor drive torque. Represents the drive torque of the hydraulic pump / motor. Represents vehicle quality. Represents the radius of the vehicle's tires. Represents the road rolling resistance coefficient. Represents gravitational acceleration. Represents air density, Represents the drag coefficient. This represents the projected area of ​​the vehicle.

[0031] In an optional embodiment of this application, the state variables of the wheel loader further include the energy state of the hydraulic accumulator. The energy state of the hydraulic accumulator Satisfy the following formula: ;

[0032] in, This represents the maximum pressure of the hydraulic accumulator. This represents the minimum pressure of the hydraulic accumulator. Represents the mechanical efficiency of a bidirectional variable hydraulic pump / motor. Represents the volumetric efficiency of a bidirectional variable hydraulic pump / motor. Represents the work mode coefficient. This indicates that the bidirectional variable hydraulic pump / motor is operating in motor mode. This indicates that the bidirectional variable hydraulic pump / motor is operating in pump mode. Represents the gas volume of the hydraulic accumulator. This represents the accumulator pressure function.

[0033] In an optional embodiment of this application, the wheel loader's speed at the starting point of operation is 0, i.e. The wheel loader's travel speed at the end of its journey is 0, that is... The energy state value of the hydraulic accumulator at the starting point of the wheel loader's operation is equal to the energy state value of the hydraulic accumulator at the ending point of the wheel loader's operation.

[0034] In optional embodiments of this application, the root-finding algorithm includes at least one of the following: bisection method, trial-and-error method, and Newton's iteration method.

[0035] In an optional embodiment of this application, the wheel loader includes a battery, a motor controller, a motor, a variable hydraulic pump / motor, a hydraulic accumulator, a hydraulic tank, a coupling, wheels, and a reducer; the battery supplies power to the motor controller, which drives the motor; the hydraulic tank provides hydraulic oil to the variable hydraulic pump / motor, which, in motor mode, converts hydraulic energy into mechanical energy to drive the wheels, and in pump mode, converts mechanical energy into hydraulic energy and stores it in the hydraulic accumulator when the wheel loader brakes; the output shaft of the motor is coaxially connected to the output shaft of the variable hydraulic pump / motor via the coupling, and then connected to the wheels via the reducer.

[0036] In an optional embodiment of this application, the variable hydraulic pump / motor outputs the same rotational speed as the motor in motor mode.

[0037] This application addresses the short battery life issue of traditional wheel loaders after electrification by proposing a collaborative optimization method for wheel loader operating parameters. First, the loader's running distance is divided into steps, ensuring each step is equal. Then, a dynamic programming-Brent algorithm, combined with initial weighting factors, is used to accurately calculate the single-step cost function for each step state, thus deriving the single-step running time. Next, the total running time of the loader is calculated cumulatively. If the total running time deviates from the preset allowable time, the weighting factors are dynamically adjusted using a root-finding algorithm until the preset conditions are met. This process achieves collaborative optimization of driving speed and energy management strategies, effectively reducing the battery charge / discharge rate and frequency, thereby slowing down battery aging.

[0038] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, optional embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a structural schematic diagram of a wheel loader using an electro-hydraulic parallel hybrid power system provided in this application;

[0041] Figure 2 This is a flowchart illustrating a collaborative optimization method for the working parameters of a wheel loader provided in this application. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0043] This application provides a method for collaborative optimization of the operating parameters of a wheel loader, used to optimize the operating parameters of a wheel loader employing an electro-hydraulic parallel hybrid power system, comprising steps S1 to S5. Here, S1, S2, etc., are merely step identifiers, and the execution order of the method does not necessarily follow an ascending numerical order; for example, step S2 may be executed before step S1, and this application does not impose any restrictions.

[0044] like Figure 1As shown, the wheel loader includes a battery 1, a motor controller 2, a motor 3, a variable hydraulic pump / motor 4, a hydraulic accumulator 5, a hydraulic oil tank 6, a coupling 7, wheels 8, and a reducer 9. The battery 1 supplies power to the motor controller 2, which drives the motor 3. The hydraulic oil tank 6 provides hydraulic oil to the variable hydraulic pump / motor 4. In motor mode, the variable hydraulic pump / motor 4 converts hydraulic energy into mechanical energy to drive the wheels 8. In pump mode, when the wheel loader brakes, the variable hydraulic pump / motor 4 converts mechanical energy into hydraulic energy and stores it in the hydraulic accumulator 5. The output shaft of the motor 3 is coaxially connected to the output shaft of the variable hydraulic pump / motor 4 via the coupling 7, and then connected to the wheels 8 via the reducer 9.

[0045] In an optional embodiment of this application, the variable hydraulic pump / motor outputs the same speed as the motor output in motor mode.

[0046] S1 is divided into steps based on the overall running distance of the wheel loader, with each step being of equal distance.

[0047] Step S1 divides the overall running distance of the wheel loader into equal-distance stepping units. The significance of this step is to discretize the continuous running process into multiple controllable stepping units, providing a basic data structure for subsequent dynamic programming, so that speed-power co-optimization can perform single-step cost calculation and parameter iteration based on a unified scale.

[0048] S2 uses the dynamic programming-Brent combinatorial algorithm to calculate the single-step cost function for each step state, and then calculates the corresponding single-step runtime based on the cost function by substituting the initial weight factors.

[0049] The Dynamic Programming-Brent Combination Algorithm combines the global optimization capabilities of dynamic programming with the rapid convergence of the Brent algorithm. In the parameter optimization of wheel loaders, dynamic programming first divides the running distance into steps, constructs a multi-stage decision model, and calculates the cost function for each step. The Brent algorithm, on the other hand, uses a combination of cubic interpolation and the golden section to quickly approximate the optimal weight factors, reducing the number of iterations. The combination of these two methods can efficiently solve for the minimum power consumption and battery wear operating parameters of the loader at each step, achieving coordinated optimization of speed and energy management.

[0050] S3, sum up the running time of each single step during the process of the wheel loader completing the overall running distance to obtain the total running time.

[0051] S4. If the difference between the total runtime and the preset runtime is greater than the preset allowable runtime error, the weight factor is updated by the root-finding algorithm, and the dynamic programming-Brenter combination algorithm is re-executed until the runtime difference is less than the preset allowable runtime error.

[0052] In optional embodiments of this application, the root-finding algorithm includes at least one of the following: bisection method, trial-and-error method, and Newton's iteration method.

[0053] The bisection method searches for the root of the equation by repeatedly dividing the initial interval into two. In each iteration, the midpoint of the interval is selected, and the sub-interval containing the root is determined based on the sign of the function value, gradually narrowing the search range. This method is simple in principle and converges stably; as long as the function is continuous and there is a root within the interval, a solution can always be found. However, its convergence speed is slow, exhibiting linear convergence.

[0054] Trial-and-error method: The trial-and-error method uses the function values ​​at two points within an interval and determines the next trial point through linear interpolation to approximate the root of the equation. It combines the reliability of the bisection method with the acceleration characteristics of the secant method, and its convergence speed is faster than that of the bisection method. However, it requires that the function be continuous within the interval and have no extreme points; otherwise, the iteration may fail.

[0055] Newton's iteration method: Newton's iteration method determines the next approximate solution by using the intersection of the tangent line at the current point and the x-axis, and accelerates convergence by utilizing the first derivative information. It converges quickly, but depends on suitable initial values. If the initial values ​​are poor, it may diverge, and the derivative needs to be calculated.

[0056] S5, obtain the optimal single-step cost function when the time difference is less than the preset allowable time error, extract the control variables corresponding to the optimal single-step cost function and output the optimal control parameters, output the optimal control parameter curve composed of the optimal control parameters corresponding to each step state, extract the state variables corresponding to the optimal single-step cost function and output the optimal driving speed, output the optimal driving speed curve composed of the optimal driving speed corresponding to each step state.

[0057] It is understandable that this application has achieved significant beneficial effects in extending the battery life of wheel loaders. On the one hand, taking the overall machine operating cost, consisting of power consumption and battery aging, as the optimization objective, the application effectively reduces the overall operating cost of the loader by co-optimizing the driving speed and energy management strategy of the electro-hydraulic parallel hybrid drive system, thereby reducing power consumption and slowing down battery aging. On the other hand, an innovative solution method for the driving speed-power distribution co-optimization problem is designed. This method can efficiently and accurately solve for the optimal parameters, improving the efficiency and accuracy of the optimization process. In an optional embodiment of this application, a dynamic programming-Brenter combinatorial algorithm is used to calculate the single-step cost function for each step state, including:

[0058] The sequence of state variables is determined according to the following formula: ;

[0059] in, Representing the A sequence of state variables under each step state. This represents the travel speed of the wheel loader. Represents the energy state of the hydraulic accumulator;

[0060] The sequence of control variables is determined according to the following formula: ;

[0061] in, Representing the The sequence of control variables in each step state Represents the motor drive torque. Represents the driving torque of the hydraulic pump / motor;

[0062] Following the sequence from the end point to the start point of the wheel loader's operation, and based on the boundary conditions of the wheel loader's system parameters, the single-step cost function for each step state is calculated sequentially using the following formula: in, Representing the Single-step cost function under each step state Representing the The step state is up to the first The comprehensive cost function for each step state. Representing the The step state is up to the first The comprehensive cost function for each step state.

[0063] In an optional embodiment of this application, the first The single-step cost function for each step state is as follows: ;

[0064] in, This represents the initial state variable sequence.

[0065] In optional embodiments of this application, the boundary conditions for the system parameters of the wheel loader include at least one of the following:

[0066] No. The sequence of state variables under each step state The travel speed of the wheel loader in the middle Belongs to the A preset driving speed threshold range ,Right now ;

[0067] No. The sequence of state variables under each step state Energy state of hydraulic accumulators Belongs to the A preset hydraulic accumulator energy threshold range ,Right now ;

[0068] No. Control variable sequence in each step state Motor drive torque Within the preset motor drive torque threshold range ,Right now ;

[0069] No. Control variable sequence in each step state The hydraulic pump / motor drive torque in Within the preset hydraulic pump / motor drive torque threshold range ,Right now .

[0070] In an optional embodiment of this application, the comprehensive cost function is constructed based on the system parameters, state variables, and control variables of the wheel loader, plus the runtime and a weighting factor for allocating the cost value and runtime used to optimize the comprehensive cost function. The comprehensive cost function is as follows: ;

[0071] in, The comprehensive cost function representing a wheel loader, This represents the current distance traveled by the wheel loader. This represents the final distance traveled by the wheel loader at its destination. Represents electricity price, Represents the battery output power. Represents battery price, Represents the charge / discharge intensity coefficient. Represents battery capacity. This represents the total number of battery discharge cycles under nominal operating conditions. Represents the battery's output current. This represents the travel speed of the wheel loader. With runtime Related: , This represents the weighting factor used to allocate the cost value and runtime of the optimization comprehensive cost function.

[0072] In an optional embodiment of this application, the driving speed It also satisfies the following formula: ;

[0073] in, Represents the gearbox transmission ratio. This represents the final drive ratio. This represents the efficiency of the transmission system. Represents the motor drive torque. Represents the drive torque of the hydraulic pump / motor. Represents vehicle quality. Represents the radius of the vehicle's tires. Represents the road rolling resistance coefficient. Represents gravitational acceleration. Represents air density, Represents the drag coefficient. This represents the projected area of ​​the vehicle.

[0074] In an optional embodiment of this application, the state variables of the wheel loader further include the energy state of the hydraulic accumulator. Hydraulic accumulator energy state Satisfy the following formula: in, This represents the maximum pressure of the hydraulic accumulator. This represents the minimum pressure of the hydraulic accumulator. Represents the mechanical efficiency of a bidirectional variable hydraulic pump / motor. Represents the volumetric efficiency of a bidirectional variable hydraulic pump / motor. Represents the work mode coefficient. This indicates that the bidirectional variable hydraulic pump / motor is operating in motor mode. This indicates that the bidirectional variable hydraulic pump / motor is operating in pump mode. Represents the gas volume of the hydraulic accumulator. This represents the accumulator pressure function.

[0075] In an optional embodiment of this application, the travel speed of the wheel loader at the starting point of operation is 0, that is... The wheel loader's travel speed at the end of its journey is 0, that is... The energy state value of the hydraulic accumulator at the starting point of the wheel loader's operation is equal to the energy state value of the hydraulic accumulator at the ending point of the wheel loader's operation.

[0076] The terms "first," "second," "first," or "second" as used in the various embodiments of this disclosure may modify various components regardless of their order and / or importance, but these terms do not limit the corresponding components. The above terms are configured only for the purpose of distinguishing an element from other elements. For example, "first user equipment" and "second user equipment" refer to different user equipments, although both are user equipment. For example, without departing from the scope of this disclosure, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element.

[0077] When a component (e.g., a first component) is referred to as being "(operably or communicatively) coupled" or "(operably or communicatively) coupled to" or "connected to" another component (e.g., a second component), it should be understood that the first component is directly connected to the second component or that the first component is indirectly connected to the second component via yet another component (e.g., a third component). Conversely, it can be understood that when a component (e.g., a first component) is referred to as being "directly connected" or "directly coupled" to another component (the second component), no component (e.g., a third component) is inserted between the two.

[0078] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0079] The above description is merely an optional embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this application.

[0080] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0081] The above description is merely an optional embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in this application.

[0082] The above description is merely an optional embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for collaborative optimization of operating parameters of a wheel loader, wherein the wheel loader adopts an electro-hydraulic parallel hybrid power system, characterized in that, Includes the following steps: S1, the overall running distance of the wheel loader is divided into steps, with each step being equal in distance; S2, use the dynamic programming-Brent combinatorial algorithm to calculate the single-step cost function for each step state, substitute the initial weight factor, and calculate the corresponding single-step runtime based on the cost function; S3, sum up the duration of each single step during which the wheel loader completes the overall running distance to obtain the total running time; S4. If the difference between the total runtime and the preset runtime is greater than the preset allowable runtime error, the weight factor is updated by the root-finding algorithm, and the dynamic programming-Brenter combination algorithm is re-executed until the runtime difference is less than the preset allowable runtime error. S5, obtain the optimal single-step cost function corresponding to the time difference being less than the preset allowable time error, extract the control variables corresponding to the optimal single-step cost function to output the optimal control parameters, output the optimal control parameter curve composed of the optimal control parameters corresponding to each step state, extract the state variables corresponding to the optimal single-step cost function to output the optimal driving speed, and output the optimal driving speed curve composed of the optimal driving speed corresponding to each step state; the step of using the dynamic programming-Brent combinatorial algorithm to calculate the single-step cost function in each step state includes: The sequence of state variables is determined according to the following formula: ; in, Representing the A sequence of state variables under each step state. This represents the travel speed of the wheel loader. Represents the energy state of the hydraulic accumulator; The sequence of control variables is determined according to the following formula: ; in, Representing the The sequence of control variables in each step state Represents the motor drive torque. Represents the driving torque of the hydraulic pump / motor; Following the sequence from the end point of the wheel loader's operation to the starting point of the wheel loader's operation, and based on the boundary conditions of the wheel loader's system parameters, the single-step cost function for each step state is calculated sequentially using the following formula: in, Representing the Single-step cost function under each step state Representing the The step state is up to the first The comprehensive cost function for each step state. Representing the The step state is up to the first The comprehensive cost function for each step state; The comprehensive cost function is constructed based on the system parameters, state variables, and control variables of the wheel loader, and incorporates the runtime and weighting factors for allocating and optimizing the cost value and runtime. The comprehensive cost function is as follows: ; in, The comprehensive cost function represents the wheel loader. This represents the current travel distance of the wheel loader. This represents the final distance traveled by the wheel loader at its destination. Represents electricity price, Represents the battery output power. Represents battery price, Represents the charge / discharge intensity coefficient. Represents battery capacity. This represents the total number of battery discharge cycles under nominal operating conditions. Represents the battery's output current. This represents the travel speed of the wheel loader, the travel speed With runtime Related: , This represents the weighting factors used to allocate the cost value and runtime of the comprehensive cost function for optimization.

2. The method for collaborative optimization of working parameters of a wheel loader according to claim 1, characterized in that, No. The single-step cost function for each step state is as follows: ; in, This represents the initial state variable sequence.

3. The method for collaborative optimization of working parameters of a wheeled loader according to claim 1, characterized in that, The boundary conditions for the system parameters of the wheel loader include at least one of the following: No. The sequence of state variables under each step state The travel speed of the wheel loader mentioned in the text Belongs to the A preset driving speed threshold range ,Right now ; No. The sequence of state variables under each step state The energy state of the hydraulic accumulator mentioned in Belongs to the A preset hydraulic accumulator energy threshold range ,Right now ; No. Control variable sequence in each step state The motor drive torque mentioned in Within the preset motor drive torque threshold range ,Right now ; No. Control variable sequence in each step state The hydraulic pump / motor drive torque in Within the preset hydraulic pump / motor drive torque threshold range ,Right now .

4. The method for collaborative optimization of working parameters of a wheel loader according to claim 1, characterized in that, The driving speed It also satisfies the following formula: ; in, Represents the gearbox transmission ratio. This represents the final drive ratio. Represents the efficiency of the transmission system. Represents the motor drive torque. Represents the drive torque of the hydraulic pump / motor. Represents vehicle quality. Represents the radius of the vehicle's tires. Represents the road rolling resistance coefficient. Represents gravitational acceleration. Represents air density, Represents the drag coefficient. This represents the projected area of ​​the vehicle.

5. The method for collaborative optimization of working parameters of a wheel loader according to claim 4, characterized in that, The energy state of the hydraulic accumulator Satisfy the following formula: ; in, This represents the maximum pressure of the hydraulic accumulator. This represents the minimum pressure of the hydraulic accumulator. Represents the mechanical efficiency of a bidirectional variable hydraulic pump / motor. Represents the volumetric efficiency of a bidirectional variable hydraulic pump / motor. Represents the work mode coefficient. This indicates that the bidirectional variable hydraulic pump / motor is operating in motor mode. This indicates that the bidirectional variable hydraulic pump / motor is operating in pump mode. Represents the gas volume of the hydraulic accumulator. This represents the accumulator pressure function.

6. The method for collaborative optimization of working parameters of a wheeled loader according to claim 1, characterized in that, The wheel loader's speed at the starting point of operation is 0, that is... ; The wheel loader's travel speed at the end of its journey is 0, that is... ; The energy state value of the hydraulic accumulator at the starting point of the wheel loader's operation is equal to the energy state value of the hydraulic accumulator at the ending point of the wheel loader's operation.

7. The method for collaborative optimization of working parameters of a wheeled loader according to claim 1, characterized in that, The root-finding algorithm includes at least one of the following: bisection method, trial-and-error method, and Newton's iteration method.

8. The method for collaborative optimization of working parameters of a wheel loader according to any one of claims 1 to 7, characterized in that, The wheel loader includes a battery, a motor controller, a motor, a variable hydraulic pump / motor, a hydraulic accumulator, a hydraulic tank, a coupling, wheels, and a reducer; The battery powers the motor controller, which in turn drives the motor to operate. The hydraulic oil tank provides hydraulic oil to the variable hydraulic pump / motor. In motor mode, the variable hydraulic pump / motor can convert hydraulic energy into mechanical energy to drive the wheels. In pump mode, the variable hydraulic pump / motor converts mechanical energy into hydraulic energy and stores it in the hydraulic accumulator when the wheeled loader brakes. The output shaft of the motor is coaxially connected to the output shaft of the variable hydraulic pump / motor via the coupling, and then connected to the wheel via a reducer.

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