Optimal configuration method and system of electric loader power system based on operation scene and computer equipment

Abstract

The invention relates to an optimal configuration method for an electric loader power system based on operation scenes, which comprises the following steps: determining operation scene information for an electric loader, setting a plurality of operation scenes for the electric loader, and representing each operation scene through a plurality of working condition settings and weight values allocated to the working condition settings; determining loader model information for the electric loader; determining a plurality of configuration combinations for the power system of the electric loader; iterative optimization is carried out on the basis of the multi-objective optimization function to determine an optimal configuration combination, and each iteration of the multi-objective optimization function is carried out on the basis of the performance index simulation value of the multi-physical domain electric loader model; and outputting optimization result information related to the determined optimization configuration combination. In addition, the invention further relates to an optimal configuration system of the electric loader power system based on the operation scene and computer equipment.

Description

Technical Field

[0001] The present application generally relates to the technical field of optimized configuration of electric loader power systems, and specifically to an optimized configuration of electric loader power systems based on operating scenarios, especially a performance evaluation and optimized matching method, system and computer equipment. Background Art

[0002] With the continuous improvement of environmental awareness and the increase in energy consumption, and the general background of the promotion of the work of "carbon peak and carbon neutrality", the development of low-carbon, green and energy-saving new energy engineering machinery has become a new trend. Compared with traditional fuel loaders, new pure electric loaders that replace the power source with batteries have a broad market. Electric loaders, especially pure electric loaders, can greatly reduce energy costs and maintenance costs. Compared with the mature subjective and objective evaluation system in the automotive field, there is no relevant subjective and objective evaluation system for electric loaders. In addition, due to the complex operation scenarios of loaders and the existence of excavation and loading operation scenarios, higher performance requirements are placed on the power system. The subjective and objective evaluation system of automobiles cannot be applied to electric loaders.

[0003] A vehicle dynamics performance evaluation method is known, which is applied to the technical field of vehicle dynamics performance analysis. The method includes: obtaining at least one of the vehicle's maneuverability index, stability index, smoothness index and braking index as the vehicle's dynamics performance index through a comprehensive evaluation interface; preprocessing the dynamics performance index to obtain the vehicle's to-be-evaluated index and decision matrix; calculating the weight of the to-be-evaluated index according to the decision matrix, and assigning the weight of the to-be-evaluated index to the to-be-evaluated index to obtain the weighted to-be-evaluated index; based on the decision matrix and the weighted to-be-evaluated index, comprehensively evaluating the vehicle's design scheme to obtain the evaluation result of the design scheme. The method can effectively reduce the influence of subjective factors on the comprehensive evaluation results, improve the accuracy of the comprehensive evaluation results while improving the credibility and objectivity of the comprehensive evaluation results, and ensure the consistency of the comprehensive evaluation results. However, the vehicle dynamics performance evaluation method cannot directly evaluate the electric loader. Due to the complex operation scenes of the electric loader and the existence of excavation and loading operation scenes, the performance requirements of the power system are higher, and different evaluation methods and devices need to be constructed for the electric loader power system.

[0004] A method for calculating power matching of a loader is known, wherein the method includes the steps of: obtaining the real-time speed of the engine when the engine is running; obtaining the torque consumed by the fan according to the rated power of the engine fan, the rated speed of the fan, and the real-time speed of the engine; obtaining the corresponding functional relationship between the torque converter input torque and the torque consumed by the fan. This method improves the rigor of the power matching calculation, reduces the calculation error, and provides a more accurate power data basis for the subsequent fuel consumption calculation and automatic transmission control calculation. However, the power matching calculation method proposed for traditional fuel loaders cannot directly evaluate electric loaders. Due to the complex operation scenes of electric loaders and the existence of excavation and loading operation scenes, higher performance requirements are placed on the power system, and different evaluation methods and devices need to be constructed for the power system of electric loaders. In addition, the power matching calculation method proposed for traditional fuel loaders requires actual measured data such as the real-time speed of the engine, and cannot achieve forward product development. Summary of the invention

[0005] Therefore, the purpose of the present application is to provide an optimization configuration method or performance evaluation and optimization matching method, system and computer equipment for an electric loader power system based on an operation scenario that can overcome at least one defect in the prior art.

[0006] According to the first aspect of the present application, there is provided an optimization configuration method for an electric loader power system based on an operation scenario, the optimization configuration method comprising: determining operation scenario information for the electric loader, wherein a plurality of operation scenarios are set for the electric loader, and each operation scenario can be characterized by a plurality of working condition settings and weight values ​​assigned to each working condition setting; determining loader model information for the electric loader; determining a plurality of configuration combinations for the power system of the electric loader; performing iterative optimization based on a multi-objective optimization function to determine an optimized configuration combination, wherein the multi-objective optimization function is constructed by setting performance indicators and weight values ​​of each performance indicator, wherein each iteration of the multi-objective optimization function is performed based on a performance indicator simulation value of a multi-physical domain electric loader model, wherein the determined operation scenario information, the determined loader model information, and one of the plurality of configuration combinations are used as inputs to drive the multi-physical domain electric loader model for simulation, so as to obtain a performance indicator simulation value as an output and substitute it into the multi-objective optimization function; and outputting optimization result information related to the determined optimized configuration combination.

[0007] In some embodiments, each operation scene includes a% of type I operations, b% of type V operations, c% of stacking operations, d% of straight uphill driving, e% of straight acceleration and deceleration driving, f% of straight constant speed driving on flat ground, g% of turning driving, and h% of straight downhill driving, where a+b+c+d+e+f+g+h=100, 0≤a,b,c,d,e,f,g,h≤100.

[0008] In some embodiments, the optimization configuration method includes: building the multi-physical domain electric loader model with the help of the loader CAD model, hydraulic schematic diagram and vehicle control strategy, and the multi-physical domain electric loader model includes a control system, a power transmission system, a mechanical system, a material system, a road surface system, a hydraulic system, and / or a thermal management system.

[0009] In some embodiments, each configuration combination includes: a power architecture, a component database, a transmission speed ratio range; and / or a pump motor reducer speed ratio range.

[0010] In some embodiments, the multi-objective optimization function is configured to include a first performance indicator characterizing power, a second performance indicator characterizing economy, and / or a third performance indicator characterizing total cost of ownership, wherein the first performance indicator includes maximum traction, maximum riser force and / or maximum climbing grade; the second performance indicator includes battery energy consumption per unit time, energy recovery per unit time and / or battery life; the third performance indicator includes vehicle acquisition cost, operating cost, management cost and / or maintenance cost.

[0011] In some embodiments, the optimization configuration method includes: determining a multi-objective optimization function based on a comprehensive scoring method for each performance indicator; determining the weight value of each performance indicator by means of a hierarchical analysis method, and substituting the corresponding weight value into the multi-objective optimization function, thereby constructing a set multi-objective optimization function.

[0012] In some embodiments, the set multi-objective optimization function is configured as an addition and subtraction combination of various performance indicator items, wherein each performance indicator item includes the product of the quotient of the simulation value and the target value of the corresponding performance indicator and the corresponding weight value.

[0013] In some embodiments, the set multi-objective optimization function is configured as the following formula:

[0014] y i * =(A 11 *(y 11 ) i +A 12 *(y 12 ) i +A 13 *(y 13 ) i )+(-A 21 *(y 21 ) i +A 22 *(y 22 ) i +A 23 *(y 23) i )

[0015] -(A 31 *(y 31 ) i +A 32 *(y 32 ) i +A 33 *(y 33 ) i +A 34 *(y 34 ) i )

[0016] Among them, y 11 is the quotient of the maximum traction force simulation value and the maximum traction force target value; 12 The quotient of the maximum rise force simulation value and the maximum rise force target value; y 13 is the quotient of the maximum climbing grade simulation value and the maximum climbing grade target value; 21 The quotient of the battery energy consumption per unit time simulation value and the battery energy consumption per unit time target value; y 22 is the quotient of the energy recovery simulation value per unit time and the energy recovery target value per unit time; 23 The quotient of the battery life simulation value and the battery life target value; 31 The quotient of the simulated value of the vehicle purchase cost and the target value of the vehicle purchase cost; y 32 The quotient of the simulated operating cost value and the target operating cost value; y 33 is the quotient of the management cost simulation value and the management cost target value; 34 A is the quotient of the simulated value of maintenance cost and the target value of maintenance cost; 11 is the maximum traction weight value, A 12 is the maximum rise force weight value, A 13 is the maximum climbing grade weight value, A 21 is the battery energy consumption weight per unit time, A 22 is the energy recovery weight per unit time, A 23 is the battery life weight value, A 31 is the vehicle purchase cost weight value, A 32 is the weight value of operating cost, A 33 is the management cost weight value, A 34 is the weight value of maintenance cost.

[0017] In some embodiments, the optimization configuration method includes: using a particle swarm algorithm, a steepest descent method, a Newton descent method, a gray wolf optimization algorithm, or a chicken swarm optimization algorithm to iteratively optimize a multi-objective optimization function to determine the optimal solution for the multi-objective optimization, and then determining the optimization configuration combination corresponding to the optimal solution.

[0018] In some embodiments, the optimization configuration method includes: selecting a first main influencing factor for a first performance indicator, preferably, the first main influencing factor includes: pump motor reducer speed ratio, gearbox speed ratio, travel motor maximum speed, travel motor maximum torque, pump motor maximum speed, pump motor maximum torque, and / or power architecture form; selecting a second main influencing factor for a second performance indicator, preferably, the second main influencing factor includes: power architecture form, pump motor reducer speed ratio, gearbox speed ratio, travel motor efficiency diagram, and / or pump motor efficiency diagram; selecting a third main influencing factor for a third performance indicator, preferably, the third main influencing factor includes: power architecture form, pump motor cost, and / or travel motor cost, wherein each selected main influencing factor is used as input to drive a multi-physical domain electric loader model for simulation to obtain a first performance indicator simulation value, a second performance indicator simulation value, and a third performance indicator simulation value.

[0019] According to a second aspect of the present application, there is provided an optimization configuration system for an electric loader power system based on an operation scenario, the optimization configuration system comprising: a human-machine interface, on which operation scenario information for the electric loader, loader model information for the electric loader, and a plurality of configuration combinations for the power system of the electric loader can be input; a first processing module, configured to store and call weight values ​​of a plurality of working condition settings for the operation scenario information, wherein each operation scenario can be characterized by a plurality of working condition settings and weight values ​​assigned to each working condition setting; a second processing module, configured to store a multi-objective optimization function and determine an optimization result based on iterative optimization of the multi-objective optimization function. configuration combination, wherein the multi-objective optimization function is constructed by setting performance indicators and weight values ​​of each performance indicator; a third processing module is configured to store a multi-physical domain electric loader model, wherein each iteration of the multi-objective optimization function is performed based on the performance indicator simulation value of the multi-physical domain electric loader model, wherein the determined operation scene information, the determined loader model information, and one of the multiple configuration combinations are used as input to drive the multi-physical domain electric loader model for simulation, so as to obtain the performance indicator simulation value as output and substitute it into the multi-objective optimization function, wherein the human-machine interface is configured to output optimization result information related to the determined optimization configuration combination.

[0020] According to a third aspect of the present application, a computer device is provided, comprising: at least one processor; and at least one memory coupled to the processor, the memory being configured to store a series of computer executable instructions, wherein the series of computer executable instructions, when executed by the processor, causes the processor to execute the optimization configuration method described in some embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present application will be described in more detail below with reference to the accompanying drawings and by means of specific embodiments. The schematic drawings are briefly described as follows:

[0022] Figure 1 is an exemplary flow chart of an optimization configuration method according to some embodiments of the present application;

[0023] Figure 2 is a schematic block diagram of a computer device according to some embodiments of the present application;

[0024] Figure 3 is Figure 1 An example diagram of a multi-physics domain electric loader model used in the optimization configuration method;

[0025] Figure 4 It is a schematic block diagram of an optimization configuration system of an electric loader power system based on working scenarios according to some embodiments of the present application. DETAILED DESCRIPTION

[0026] The present application will be described below with reference to the accompanying drawings, wherein the accompanying drawings illustrate several embodiments of the present application. However, it should be understood that the present application can be presented in a variety of different ways and is not limited to the embodiments described below; in fact, the embodiments described below are intended to make the disclosure of the present application more complete and fully explain the scope of protection of the present application to those skilled in the art. It should also be understood that the embodiments disclosed herein can be combined in various ways to provide more additional embodiments.

[0027] In the various embodiments described in different ways, the same elements are assigned the same reference numerals or the same element names, wherein the disclosure contained in the entire specification can be transferred to the elements assigned the same reference numerals or the same element names according to the meaning. In addition, in various embodiments, the number, implementation and / or arrangement structure of the elements are not limited to the examples shown, but other numbers, implementations and / or arrangements can be selected according to actual needs.

[0028] Herein, the term "A or B" includes "A and B" and "A or B" rather than exclusively including only "A" or only "B", unless specifically stated otherwise.

[0029] In this document, the terms "illustrative" or "exemplary" mean "used as an example, instance or illustration" rather than as a "model" to be exactly copied. Any implementation described as an example herein is not necessarily to be interpreted as preferred or advantageous over other implementations. Moreover, this application is not limited by any expressed or implied theory given in the above technical field, background technology, invention content or detailed description.

[0030] As used herein, the term "substantially" is intended to include any minor variations due to design or manufacturing defects, tolerances of devices or components, environmental influences, and / or other factors.

[0031] In addition, the terms "first", "second" and the like may also be used herein for reference purposes only, and thus are not intended to be limiting. For example, the terms "first", "second" and other such numerical terms referring to structures or elements do not imply a sequence or order unless the context clearly indicates otherwise.

[0032] This application proposes an optimization configuration method for an electric loader power system based on an operation scenario, which can advantageously perform performance evaluation and optimization matching of different configuration combinations of the power system. This can achieve efficient configuration combination selection of the electric loader power system, thereby reducing R&D costs and shortening the new product development cycle.

[0033] Figure 1 An exemplary flow chart of an optimization configuration method according to some embodiments of the present application is shown. It should be understood that the steps of the method presented below are intended to be illustrative. In certain embodiments, the method may be completed with one or more additional undescribed steps, and / or without one or more discussed steps. In addition, the order of the steps of the method shown in the figure and described below is not intended to be limiting. In certain embodiments, one or more steps may be alternative or preferred, to achieve further advantageous technical effects.

[0034] At least a portion of the method steps of the optimization configuration method of the present application can be executed by the computer device 200 of the present application, such as Figure 2 As shown, the computer device 200 may include one or more processors 210; and one or more computer-readable memories 220 coupled to the one or more processors, wherein the computer-readable memories are configured to store a series of computer-executable instructions, wherein when the series of computer-executable instructions are executed by the one or more processors, the one or more processors execute at least a portion of the method steps of the optimization configuration method of the present application.

[0035] like Figure 1As shown, the optimization configuration method of the present application may include step S101: determining the operation scenario information for the electric loader. The loader can be applied to different types of operation scenarios, such as mining operation scenarios, port operation scenarios, construction site operation scenarios, etc. Each operation scenario may have a specific working condition setting. Therefore, the loader operation scenario is relatively complex. In the present application, it is advantageous to set a plurality of operation scenarios for the electric loader, and each operation scenario can be characterized by a plurality of working condition settings and a weight value assigned to each working condition setting. In the optimization configuration method of the present application, the corresponding operation scenario can be efficiently and accurately characterized by a combination of a plurality of working condition settings and assigned weight values, thereby improving the adaptability of the electric loader to the operation scenario.

[0036] In some embodiments, each operation scene may include a% of I-type operation, b% of V-type operation, c% of stockpiling operation, d% of straight uphill driving, e% of straight acceleration and deceleration driving, f% of straight uniform speed flat driving, g% of turning driving, and h% of straight downhill driving, where a+b+c+d+e+f+g+h=100, 0≤a, b, c, d, e, f, g, h≤100. It should be understood that the composition of the corresponding operation scene can be flexibly set and does not have to strictly include the composition method introduced in the current embodiment.

[0037] like Figure 1 As shown, the optimization configuration method of the present application may include step S102: determining loader model information for an electric loader. The loader model information may characterize a specific type of electric loader for driving or operating the multi-physical domain electric loader model described later.

[0038] like Figure 1 As shown, the optimization configuration method of the present application may include step S103: determining multiple configuration combinations for the power system of the electric loader. In the present application, each configuration combination may include: a given power architecture, a given parts database, a given gearbox speed ratio range; and / or a given pump motor reducer speed ratio range.

[0039] A variety of power architectures can usually be provided for electric loaders, which may involve, for example, pure electric architectures, such as single-motor pure electric architectures, dual-motor pure electric architectures, wheel-side motor pure electric architectures, etc.; they may also involve hybrid architectures, etc. Different power architectures have large differences in power, economy and cost. Parameters of various components, such as performance parameters and / or cost parameters, etc., can be provided in the parts database, and different components (such as different types of motors) have large differences in power, economy and cost. In addition, the gearbox ratio range and / or the pump motor reducer ratio range may also affect the power, economy and cost of the electric loader.

[0040] Therefore, in the optimization configuration method of the present application, for the power system of the electric loader, performance evaluation and optimization matching are performed by predetermining multiple configuration combinations, so that the optimized configuration combination of the power system suitable for the working scenario can be efficiently selected.

[0041] like Figure 1 As shown, the optimization configuration method of the present application may include step S104: performing iterative optimization based on a multi-objective optimization function to determine an optimized configuration combination.

[0042] The multi-objective optimization function can be constructed by setting performance indicators and weight values ​​of each performance indicator. In some embodiments, the multi-objective optimization function can be configured to include a first performance indicator characterizing power, a second performance indicator characterizing economy, and / or a third performance indicator characterizing total cost of ownership. In some embodiments, the first performance indicator may include maximum traction, maximum rise force and / or maximum climbing grade. In some embodiments, the second performance indicator may include battery energy consumption per unit time, energy recovery per unit time and / or battery life. In some embodiments, the third performance indicator may include vehicle acquisition cost, operating cost, management cost and / or maintenance cost. It should be understood that the multi-objective optimization function may include different or other performance indicators, and is not limited to the specific examples of the current embodiment.

[0043] In some embodiments, it can be stipulated that: a multi-objective optimization function is determined based on a comprehensive scoring method for each performance indicator; the weight value of each performance indicator is determined by means of a hierarchical analysis method, and the corresponding weight value is substituted into the multi-objective optimization function, thereby constructing a set multi-objective optimization function.

[0044] Comprehensive scoring is a decision analysis method used to evaluate and compare multiple options or objects. This method quantifies different evaluation indicators to form a comprehensive score, thereby helping decision makers make choices among multiple alternatives. The basic steps of comprehensive scoring usually include:

[0045] Determine the evaluation indicators: According to the decision-making objectives, select appropriate evaluation indicators (such as cost, efficiency, quality, etc.), which should be able to reflect the characteristics of each plan;

[0046] Standardization: Since different evaluation indicators may have different dimensions and ranges, they need to be standardized so that different indicators can be compared. Common standardization methods include normalization, Z-score standardization, etc.

[0047] Determine the weight: Determine the importance weight of each evaluation indicator, which reflects the contribution of each indicator to the final score. The weight can be obtained through expert scoring, questionnaire survey, hierarchical analysis method, etc.

[0048] Calculate the comprehensive score: multiply the standardized indicator value by the weight and sum them to get the comprehensive score of each alternative;

[0049] Sorting and selection: All options are sorted according to their comprehensive scores, and the option with the highest score is selected as the final decision.

[0050] It should be understood that the implementation of the comprehensive scoring method is known and mature to those skilled in the art, and can be performed with reference to relevant schemes in the prior art, which will not be described in detail here.

[0051] In the present application, the following multi-objective optimization function can be obtained by using a comprehensive scoring method, wherein the multi-objective optimization function is configured as an addition and subtraction combination of various performance index items, wherein each performance index item includes the product of the quotient of the simulation value and the target value of the corresponding performance index and the corresponding weight value.

[0052] In some embodiments, the multi-objective optimization function may be configured as follows:

[0053] y i * =(A 11 *(y 11 ) i +A 12 *(y 12 ) i +A 13 *(y 13 ) i )+(-A 21 *(y 21 ) i +A 22 *(y 22 ) i +A 23 *(y 23 ) i )

[0054] -(A 31 *(y 31 ) i +A 32 *(y 32 ) i +A 33 *(y 33 ) i +A 34 *(y 34 ) i )

[0055] Among them, y 11 is the quotient of the maximum traction force simulation value and the maximum traction force target value; 12 The quotient of the maximum rise force simulation value and the maximum rise force target value; y 13 is the quotient of the maximum climbing grade simulation value and the maximum climbing grade target value; 21 The quotient of the battery energy consumption per unit time simulation value and the battery energy consumption per unit time target value; y 22 is the quotient of the energy recovery simulation value per unit time and the energy recovery target value per unit time; 23 The quotient of the battery life simulation value and the battery life target value; 31 The quotient of the simulated value of the vehicle purchase cost and the target value of the vehicle purchase cost; y 32 The quotient of the simulated operating cost value and the target operating cost value; y 33 is the quotient of the management cost simulation value and the management cost target value; 34 A is the quotient of the simulated value of maintenance cost and the target value of maintenance cost; 11 is the maximum traction weight value, A 12 is the maximum rise force weight value, A 13 is the maximum climbing grade weight value, A 21 is the battery energy consumption weight per unit time, A 22 is the energy recovery weight per unit time, A 23 is the battery life weight value, A 31 is the vehicle purchase cost weight value, A 32 is the weight value of operating cost, A 33 is the management cost weight value, A 34 is the weight value of the maintenance cost. It should be understood that the multi-objective optimization function may include different or other performance indicators, and is not limited to the specific examples of the current embodiment. For example, in some embodiments, some performance indicator items may not be included in the multi-objective optimization function, or more performance indicator items may be additionally considered.

[0056] In some embodiments, it may be provided that: a particle swarm algorithm, a steepest descent method, a Newton descent method, a gray wolf optimization algorithm, or a chicken flock optimization algorithm is used to iteratively optimize a multi-objective optimization function to determine the optimal solution of the multi-objective optimization, and then determine the optimal configuration combination corresponding to the optimal solution. Advantageously, each iteration of the multi-objective optimization function may be performed based on the performance index simulation value of the multi-physical domain electric loader model, wherein the determined operation scene information, the determined loader model information, and one of the multiple configuration combinations are used as input to drive the multi-physical domain electric loader model to perform simulation, so as to obtain the performance index simulation value as an output and substitute it into the multi-objective optimization function.

[0057] In order to obtain reliable simulation values ​​of performance indicators, the present application may provide for building a multi-physical domain electric loader model with the aid of a loader CAD model, a hydraulic schematic diagram, and a vehicle control strategy. For example, a multi-physical domain electric loader model may be built with the aid of simulation software known to those skilled in the art, such as Simscape, based on a loader CAD model, a hydraulic schematic diagram, and a vehicle control strategy. Figure 3 As shown, an exemplary multi-physics domain electric loader model is shown, and the multi-physics domain electric loader model may include a control system, a powertrain system, a mechanical system, a material system, a road system, a hydraulic system, and / or a thermal management system.

[0058] By building an electric loader model with multiple physical domains such as machine, electricity, fluid, heat, material, field, and control, this application can simulate the actual operation scene to the greatest extent, so that the optimized configuration of the power system is more in line with the actual application requirements. In addition, the method enables users to conduct in-depth analysis of the power architecture of the electric loader in different operation scenarios, and achieve accurate matching and optimization of the power system.

[0059] In addition, the multi-physics domain electric loader model is used for multi-faceted simulation analysis, and the weight values ​​of each performance index are obtained by using a comprehensive scoring method and a multi-objective optimization function is constructed, which can achieve efficient optimization of the electric loader power system. In particular, the multi-objective optimization results obtained by the particle swarm algorithm ensure the optimal configuration of the power system in terms of performance and economy.

[0060] In addition, due to the large number of influencing factors in the iterative optimization process, the efficiency of the iterative optimization process is affected. For this reason, some embodiments of the present application provide for sensitivity analysis of each performance index to obtain the main influencing factors (i.e., the performance index elements that play a major influencing role), and limit the iterative optimization process to these main influencing factors. In some embodiments, the first main influencing factor can be selected for the first performance index, preferably, the first main influencing factor includes: pump motor reducer speed ratio, gearbox speed ratio, walking motor maximum speed, walking motor maximum torque, pump motor maximum speed, pump motor maximum torque, and / or power architecture form. In some embodiments, the second main influencing factor can be selected for the second performance index, preferably, the second main influencing factor includes: power architecture form, pump motor reducer speed ratio, gearbox speed ratio, walking motor efficiency diagram, and / or pump motor efficiency diagram. In some embodiments, the third main influencing factor is selected for the third performance index, preferably, the third main influencing factor includes: power architecture form, pump motor cost, and / or walking motor cost.

[0061] In this application, once the main influencing factors are determined, these main influencing factors can be used as input to drive the power matching model of the multi-physical domain electric loader for simulation. The purpose of this process is to obtain the corresponding performance index simulation values, including but not limited to battery energy consumption, maximum traction, maximum digging force, maximum climbing grade, speed and torque of each travel motor and / or pump motor, etc. In addition, by combining the efficiency diagram of each motor, the average efficiency value and median efficiency of each motor can be calculated to provide necessary data support for subsequent optimization.

[0062] Based on the multi-objective optimization function, the comprehensive score of the electric loader power system can be evaluated. In this process, the particle swarm optimization (PSO) algorithm can be used to optimize the power system configuration of the electric loader. The particle swarm optimization algorithm is an iterative optimization method based on swarm intelligence. Its process begins with the initialization of a set of random solutions, and through continuous iterative search to find the optimal solution, the global optimal configuration of the system is achieved.

[0063] It should be noted that before optimization, it is possible to consider combing the cost and efficiency graphs of the pump motor and / or travel motor. This process involves permuting and combining the optional pump motor and / or travel motor to generate multiple samples to be optimized. Subsequently, multi-objective particle swarm optimization is performed for each sample to calculate the optimal comprehensive score for each sample. Finally, through comparative analysis between the samples, the power system configuration with the best comprehensive score is determined to ensure the optimized performance of the electric loader in terms of performance and efficiency.

[0064] The optimization configuration method of the present application may include step S105: outputting optimization result information related to the determined optimization configuration combination. Advantageously, the optimization result information may be fed back through any feasible notification method, such as a display interface, SMS notification, and / or voice playback. In some embodiments, the optimization result information may include: weight values ​​of various performance indicators, comprehensive evaluation results of the power system optimization configuration (such as preferred travel motor model, hydraulic motor model, pump motor reducer speed ratio and / or gearbox speed ratio, etc.).

[0065] Additionally or alternatively, the optimization configuration method may further include providing optimization result information related to the determined optimization configuration combination to a control system for producing an electric loader power system, so that the control system can perform targeted power system production based on the determined optimization configuration combination.

[0066] Next, refer to Figure 4 , introduces an optimization configuration system 100 of an electric loader power system based on a work scenario according to some embodiments of the present application. Figure 4As shown, the optimization configuration system 100 may include a human-machine interface 10, on which operation scene information for the electric loader, loader model information for the electric loader, and multiple configuration combinations for the power system of the electric loader may be input.

[0067] like Figure 4 As shown, the optimization configuration system 100 may further include a first processing module 21, which may be configured to store and call weight values ​​of multiple working condition settings for operation scene information, wherein each operation scene may be represented by multiple working condition settings and weight values ​​assigned to each working condition setting.

[0068] In addition, the optimization configuration system 100 may also include a second processing module 22, which may be configured to store a multi-objective optimization function and determine an optimal configuration combination based on iterative optimization of the multi-objective optimization function, wherein the multi-objective optimization function is constructed by setting performance indicators and weight values ​​of each performance indicator.

[0069] In some embodiments, the second processing module 22 can be configured to use a particle swarm algorithm, a steepest descent method, a Newton descent method, a grey wolf optimization algorithm, or a chicken swarm optimization algorithm to iteratively optimize the multi-objective optimization function to determine the optimal solution of the multi-objective optimization, and then determine the optimal configuration combination corresponding to the optimal solution.

[0070] In addition, the optimization configuration system 100 may also include a third processing module 23, which may be configured to store a multi-physical domain electric loader model, wherein each iteration of the multi-objective optimization function is performed based on the performance indicator simulation value of the multi-physical domain electric loader model, wherein the determined operation scene information, the determined loader model information, and one of the multiple configuration combinations are used as input to drive the multi-physical domain electric loader model for simulation, so as to obtain the performance indicator simulation value as output and substitute it into the multi-objective optimization function.

[0071] like Figure 4 As shown, the second processing module 22 and the third processing module 23 can be integrated into an iterative optimization module. When performing iterative optimization, the second processing module 22 needs to activate (for example, through a predetermined activation signal) the operation of the third processing module 23, so as to call the performance indicator simulation value from the third processing module 23 to perform multi-objective optimization.

[0072] Advantageously, the third processing module 23 can be configured to store various types of electric loader models, various power structure models, and component database parameters, etc. In addition, the third processing module 23 can also be configured to drive the multi-physical domain electric loader model to simulate based on the operation scene information, loader model information, and configuration combination as input to provide corresponding performance index simulation values.

[0073] Advantageously, the third processing module 23 may be configured to use the selected main influencing factors as input to drive the multi-physical domain electric loader model to perform simulation, so as to obtain a first performance indicator simulation value, a second performance indicator simulation value, and a third performance indicator simulation value.

[0074] The or additional human-machine interface 10 may be configured to output optimization result information related to the determined optimization configuration combination. For example, the optimization result information may include: weight values ​​of various performance indicators, comprehensive evaluation results of power system optimization configuration (such as preferred travel motor model, hydraulic motor model, pump motor reducer speed ratio and / or gearbox speed ratio, etc.).

[0075] In some embodiments, the optimization configuration system 100 can be implemented as follows: Figure 2 The computer device 200 shown. The present application designs an electric loader power system optimization configuration software in the computer device 200, inputs the optimization algorithm into the memory of the computer device, and provides a convenient tool for users to quickly match and optimize the power system of the electric loader. This system not only reduces the R&D cost, but also shortens the development cycle of new products and improves the market competitiveness of enterprises.

[0076] Although some specific embodiments of the present application have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are intended only for illustration and not for limiting the scope of the present application. The various embodiments disclosed herein may be combined in any manner without departing from the spirit and scope of the present application. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present application. The scope of the present application is defined by the appended claims.

Claims

1. A method for optimizing the configuration of an electric loader power system based on an operation scenario, characterized in that: The optimization configuration method comprises: Determine operation scene information for the electric loader, wherein a plurality of operation scenes are set for the electric loader, and each operation scene can be represented by a plurality of working condition settings and a weight value assigned to each working condition setting; Determining loader model information for an electric loader; Determine multiple configuration combinations of power systems for electric loaders; Iterative optimization is performed based on a multi-objective optimization function to determine an optimal configuration combination, wherein the multi-objective optimization function is constructed by set performance indicators and weight values ​​of each performance indicator, wherein each iteration of the multi-objective optimization function is performed based on a performance indicator simulation value of a multi-physical domain electric loader model, wherein determined operation scene information, determined loader model information, and one of a plurality of configuration combinations are used as inputs to drive the multi-physical domain electric loader model for simulation, so as to obtain a performance indicator simulation value as an output and substitute it into the multi-objective optimization function; Output the optimization result information related to the determined optimization configuration combination.

2. The optimization configuration method according to claim 1, characterized in that: Each operation scenario includes a% type I operation, b% type V operation, c% stacking operation, d% straight uphill driving, e% straight acceleration and deceleration driving, f% straight constant speed flat driving, g% turning driving, and h% straight downhill driving, where a+b+c+d+e+f+g+h=100, and 0≤a,b,c,d,e,f,g,h≤100.

3. The optimization configuration method according to claim 1, characterized in that: The optimization configuration method comprises: The multi-physical domain electric loader model is built with the help of the loader CAD model, hydraulic schematic diagram and vehicle control strategy. The multi-physical domain electric loader model includes a control system, a power transmission system, a mechanical system, a material system, a road system, a hydraulic system, and / or a thermal management system.

4. The optimization configuration method according to claim 1, characterized in that: Each configuration combination includes: powertrain architecture, component database, transmission ratio range; and / or pump motor reducer ratio range.

5. The optimization configuration method according to claim 1, characterized in that: The multi-objective optimization function is configured to include a first performance index representing power, a second performance index representing economy, and / or a third performance index representing total cost of ownership. Among them, the first performance indicator includes maximum traction, maximum rising force and / or maximum climbing grade; the second performance indicator includes battery energy consumption per unit time, energy recovery per unit time and / or battery life; the third performance indicator includes vehicle purchase cost, operating cost, management cost and / or maintenance cost.

6. The optimization configuration method according to claim 5, characterized in that: The optimization configuration method comprises: Determine the multi-objective optimization function based on the comprehensive scoring method for each performance indicator; The weight values ​​of various performance indicators are determined by means of the hierarchical analysis method, and the corresponding weight values ​​are substituted into the multi-objective optimization function to construct the set multi-objective optimization function.

7. The optimization configuration method according to claim 6, characterized in that: The set multi-objective optimization function is configured as a combination of addition and subtraction of various performance index items, wherein each performance index item includes the product of the quotient of the simulation value and the target value of the corresponding performance index and the corresponding weight value, Preferably, the set multi-objective optimization function is configured as the following formula: and i * (A) 11 *(and 11 ) i +A 12 *(and 12 ) i +A 13 *(and 13 ) i )+(-A 21 *(and 21 ) i +A 22 *(and 22 ) i +A 23 *(and 23 ) i ) -(TO 31 *(and 31 ) i +A 32 *(and 32 ) i +A 33 *(and 33 ) i +A 34 *(and 34 ) i ) Among them, y 11 is the quotient of the maximum traction force simulation value and the maximum traction force target value; 12 is the quotient of the maximum rise force simulation value and the maximum rise force target value; 13 is the quotient of the maximum climbing grade simulation value and the maximum climbing grade target value; 21 The quotient of the battery energy consumption per unit time simulation value and the battery energy consumption per unit time target value; y 22 is the quotient of the energy recovery simulation value per unit time and the energy recovery target value per unit time; 23 The quotient of the battery life simulation value and the battery life target value; 31 The quotient of the simulated value of the vehicle purchase cost and the target value of the vehicle purchase cost; y 32 The quotient of the simulated operating cost value and the target operating cost value; y 33 is the quotient of the management cost simulation value and the management cost target value; 34 It is the quotient of the simulated value of maintenance cost and the target value of maintenance cost; Among them, A 11 is the maximum traction weight value, A 12 is the maximum rise force weight value, A 13 is the maximum climbing grade weight value, A 21 is the battery energy consumption weight per unit time, A 22 is the energy recovery weight per unit time, A 23 is the battery life weight value, A 31 is the vehicle purchase cost weight value, A 32 is the weight value of operating cost, A 33 is the management cost weight value, A 34 is the weight value of maintenance cost.

8. The optimization configuration method according to claim 7, characterized in that: The optimization configuration method comprises: Use particle swarm optimization, steepest descent method, Newton descent method, grey wolf optimization algorithm, or chicken swarm optimization algorithm to iteratively optimize the multi-objective optimization function to determine the optimal solution of the multi-objective optimization, and then determine the optimal configuration combination corresponding to the optimal solution.

9. The optimization configuration method according to claim 6, characterized in that: The optimization configuration method comprises: A first main influencing factor is selected for the first performance indicator. Preferably, the first main influencing factor includes: a pump motor reducer speed ratio, a gearbox speed ratio, a travel motor maximum speed, a travel motor maximum torque, a pump motor maximum speed, a pump motor maximum torque, and / or a power architecture form; Selecting a second main influencing factor for the second performance indicator, preferably, the second main influencing factor includes: a power architecture form, a pump motor reducer speed ratio, a gearbox speed ratio, a travel motor efficiency diagram, and / or a pump motor efficiency diagram; A third main influencing factor is selected for the third performance indicator. Preferably, the third main influencing factor includes: power structure type, pump motor cost, and / or travel motor cost. The selected main influencing factors are used as input to drive the multi-physical domain electric loader model for simulation to obtain a first performance indicator simulation value, a second performance indicator simulation value, and a third performance indicator simulation value.

10. An optimization configuration system for an electric loader power system based on an operation scenario, characterized in that: The optimization configuration system comprises: A human-machine interface, on which operation scene information for the electric loader, loader model information for the electric loader, and multiple configuration combinations for the power system of the electric loader can be input; A first processing module is configured to store and call weight values ​​of multiple working condition settings for the working scene information, wherein each working scene can be represented by multiple working condition settings and weight values ​​assigned to each working condition setting; A second processing module is configured to store a multi-objective optimization function and determine an optimal configuration combination based on iterative optimization of the multi-objective optimization function, wherein the multi-objective optimization function is constructed by setting performance indicators and weight values ​​of each performance indicator; The third processing module is configured to store a multi-physical domain electric loader model, wherein each iteration of the multi-objective optimization function is performed based on a performance index simulation value of the multi-physical domain electric loader model, wherein the determined operation scene information, the determined loader model information, and one of a plurality of configuration combinations are used as inputs to drive the multi-physical domain electric loader model to perform simulation, so as to obtain the performance index simulation value as an output and substitute it into the multi-objective optimization function, The human-machine interface is configured to output optimization result information related to the determined optimization configuration combination.

11. The optimization configuration system according to claim 10, characterized in that: Each operation scenario includes a% I-type operation, b% V-type operation, c% stockpiling operation, d% straight uphill driving, e% straight acceleration and deceleration driving, f% straight uniform speed flat ground driving, g% turning driving, and h% straight downhill driving, where a+b+c+d+e+f+g+h=100, 0≤a,b,c,d,e,f,g,h≤100; and / or The multi-physical domain electric loader model is constructed with the help of the loader CAD model, hydraulic schematic diagram and vehicle control strategy, and the multi-physical domain electric loader model includes a control system, a powertrain system, a mechanical system, a material system, a pavement system, a hydraulic system, and / or a thermal management system; and / or Each configuration combination includes: powertrain architecture, component database, transmission ratio range; and / or pump motor reducer ratio range.

12. The optimization configuration system according to claim 11, characterized in that: The multi-objective optimization function is configured to include a first performance index representing power, a second performance index representing economy, and a third performance index representing total cost of ownership. Among them, the first performance indicator includes the maximum traction, maximum rising force and / or maximum climbing grade; the second performance indicator includes the battery energy consumption per unit time, energy recovery per unit time and / or battery life; the third performance indicator includes the vehicle purchase cost, operating cost, management cost and / or maintenance cost. Preferably, the multi-objective optimization function is determined based on the comprehensive scoring method for each performance indicator, and the weight value of each performance indicator is determined by means of the hierarchical analysis method, and the corresponding weight value is substituted into the multi-objective optimization function, so as to construct the set multi-objective optimization function. Preferably, the set multi-objective optimization function is configured as an addition and subtraction combination of various performance index items, wherein each performance index item includes the product of the quotient of the simulation value and the target value of the corresponding performance index and the corresponding weight value.

13. The optimization configuration system according to claim 12, characterized in that: The second processing module is configured to use a particle swarm algorithm, a steepest descent method, a Newton descent method, a grey wolf optimization algorithm, or a chicken swarm optimization algorithm to iteratively optimize the multi-objective optimization function to determine the optimal solution of the multi-objective optimization, and then determine the optimal configuration combination corresponding to the optimal solution.

14. The optimization configuration system according to claim 12, characterized in that: The third processing module is configured to use the selected main influencing factors as input to drive the multi-physics domain electric loader model to simulate, so as to obtain a first performance index simulation value, a second performance index simulation value, and a third performance index simulation value. Among them, the main influencing factors selected include: The first main influencing factor, preferably, the first main influencing factor includes: pump motor reducer speed ratio, gearbox speed ratio, travel motor maximum speed, travel motor maximum torque, pump motor maximum speed, pump motor maximum torque, and / or power architecture form; The second main influencing factor, preferably, the second main influencing factor includes: power architecture form, pump motor reducer speed ratio, gearbox speed ratio, travel motor efficiency diagram, and / or pump motor efficiency diagram; The third main influencing factor, preferably, the third main influencing factor includes: power architecture form, pump motor cost, and / or travel motor cost.

15. Computer equipment, the computer equipment comprising: at least one processor; and at least one memory coupled to the processor, the memory being configured to store a series of computer executable instructions, Wherein, when the series of computer executable instructions are executed by a processor, the processor is caused to perform the optimization configuration method according to any one of claims 1 to 9.

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