Engineering machinery multi-level whole system energy efficiency evaluation and improvement method

Abstract

The invention discloses a multi-level whole system energy efficiency evaluation and improvement method for engineering machinery, relates to the technical field of energy efficiency evaluation, and solves the technical problem that multi-level energy efficiency evaluation cannot be effectively carried out on a mechanical-electric-hydraulic highly-integrated complex system in the prior art. The method comprises the steps of performing hierarchical multi-target decomposition on a whole machine according to an energy flow to obtain a multi-level energy efficiency hierarchical decomposition architecture; establishing an energy efficiency evaluation index of the whole machine layer; building a CAE model of the whole machine for simulation, and obtaining simulation data; building a quantitative relation approximation model between energy efficiency evaluation indexes from the nth layer to the (n + 1) th layer through multi-level nonlinear target mapping based on simulation data; obtaining the optimal feasible interval of the energy efficiency evaluation index of the (n + 1) th layer based on the optimal feasible interval of the energy efficiency evaluation index of the nth layer; judging whether the actual energy efficiency condition of each level reaches the standard or not according to the optimal feasible interval of the energy efficiency evaluation index of each level; according to the method, the time and manpower investment is greatly reduced, and the universality of energy efficiency evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy efficiency evaluation, and particularly relates to a multi-level full-system energy efficiency evaluation and improvement method for construction machinery. Background Art

[0002] Currently, in the field of traditional passenger vehicles, energy consumption indicators are mostly used to evaluate the energy-saving effect. However, in today's situation where the energy efficiency problem of construction machinery has become the focus of industry competition, evaluating with energy consumption indicators far from effectively solves the energy efficiency evaluation problem. Therefore, how to maximize energy utilization rate and reduce emissions while ensuring the operation effect is the energy efficiency problem, that is, the comprehensive embodiment of energy consumption and operation effectiveness.

[0003] The prior art such as the Chinese patent "Method, Device and Storage Medium for Energy Efficiency Evaluation of Construction Machinery Based on Digital Twin" (Patent No.: ZL202211072676.2) generates construction elements representing the entity of the construction object, generates a virtual body model through the construction elements and operation parameters, compares the energy efficiency deviation, performance and fatigue degree between the entity and the virtual body, forms an energy efficiency evaluation, and provides a device (processor and storage medium) for executing the energy efficiency evaluation method.

[0004] However, this patent mainly focuses on a large amount of data accumulation. The accuracy of the digital twin model highly depends on the quality and quantity of data. Therefore, high-precision measuring instruments are required to collect data samples, which will greatly increase the evaluation cost.

[0005] Such as the Chinese patent "A Method for Optimizing Energy Consumption of Hybrid Electric Vehicles" (Patent No.: ZL202210129337.7), which calculates the required torque of the wheels according to the vehicle operation conditions and the remaining battery power; then uses the genetic algorithm to generate vehicle operation parameters in real time; finally selects the individual with the smallest fitness value in the initial population through fitness calculation, that is, the individual with the smallest total fuel consumption as the optimal individual, and decodes and outputs the vehicle operation parameters corresponding to the optimal individual.

[0006] However, when selecting the optimal individual in this patent, the individual with the smallest fuel consumption is taken as the optimal individual. It only considers energy consumption optimization and does not have an energy efficiency evaluation index that comprehensively evaluates from the two dimensions of "energy consumption" and "operation effectiveness".

[0007] As described in the Chinese patent "A Method and System for Energy Consumption Control of an Extended-Range Electric Loader" (Patent No.: ZL202211523918.5), obtain the system state parameters of the vehicle; perform model prediction based on a recurrent neural network to obtain the vehicle speed and the required power of the drive motor in the future time window; use the rolling optimization idea of model predictive control (MPC) and the particle swarm optimization algorithm for optimization and solution; use the equivalent energy consumption minimum strategy (ECMS) algorithm to calculate the optimal output power combination sequence of the range extender and the battery pack; control the power output of the range extender and the battery pack in the prediction window to achieve the control effect of low energy consumption.

[0008] However, the energy efficiency evaluation index of this patent is too single. By using the power-optimal fuel consumption mapping model of the range extender for optimization and solution, only fuel consumption or power consumption is considered, without comprehensively considering the energy efficiency impact of other important systems, and no multi-level (including "whole machine", "system", "sub-system" and "components", etc.) energy efficiency evaluation of the loader is achieved. Summary of the Invention

[0009] To solve the problems existing in the above-mentioned prior art, the present invention provides a method for multi-level and full-system energy efficiency evaluation and improvement of construction machinery, which solves the technical problem that the prior art cannot effectively perform multi-level energy efficiency evaluation for a complex system with highly integrated mechanical, electrical and hydraulic components.

[0010] A method for multi-level and full-system energy efficiency evaluation of construction machinery includes the following steps:

[0011] Step 1: Perform hierarchical multi-objective decomposition on the whole machine according to the energy flow to obtain a multi-level energy efficiency hierarchical decomposition architecture, where the multi-level includes the 1st layer, the 2nd layer,..., the i-th layer from the high level to the low level, and the 1st layer is the whole machine layer and the i-th layer is the component layer;

[0012] Step 2: Establish an energy efficiency evaluation index for the whole machine layer and determine the corresponding optimal feasible interval, where the energy efficiency evaluation index includes energy consumption and operation effectiveness;

[0013] Step 3: Build a CAE model of the whole machine for simulation to obtain simulation data;

[0014] Step 4: Based on the simulation data, construct an approximate quantitative relationship model between the energy efficiency evaluation indexes of the n-th layer and the n+1-th layer through multi-level non-linear objective mapping, where n takes values of 1, 2,..., i-1;

[0015] Step 5: Calculate the optimal feasible interval of the energy efficiency evaluation index of the n+1-th layer through an intelligent optimization algorithm and an interval search method based on the optimal feasible interval of the energy efficiency evaluation index of the n-th layer for the approximate quantitative relationship model obtained in Step 4;

[0016] Step 6: Determine whether there is an optimal feasible interval for the energy efficiency evaluation index at each level. If so, proceed to Step 7; otherwise, return to Step 4.

[0017] Step 7: Based on the optimal feasible intervals of the energy efficiency evaluation indexes of the whole machine layer, the second layer, …, the i-th layer, determine whether the actual energy efficiency of the whole machine layer, the second layer, …, the i-th layer meets the standards.

[0018] Furthermore, the formula for the energy efficiency evaluation index of the whole machine layer is:

[0019] η VEE = α 1 · η VE + β 1 · η VW

[0020]

[0021] In the formula, α 1 , β 1 represent the energy efficiency evaluation weights of the whole machine layer, and their sum is 1. η VE is the energy consumption evaluation index of the whole machine; η VW is the operation effect evaluation index of the whole machine; E Vout is the effective energy consumption / kJ of the whole machine to complete the set working conditions; E Vloss is the ineffective energy consumption / kJ of the whole machine to complete the set working conditions; M is the weight / t of the shoveled material of the whole machine to complete the set working conditions; t V is the time / s spent by the whole machine to complete the set working conditions.

[0022] The formula for the energy efficiency evaluation index of the second layer, …, the i-th layer is:

[0023] η SEE = α i · η SE + β i · η SW

[0024]

[0025] In the formula, α i , β i represent the energy efficiency evaluation weights of the i-th layer, and their sum is 1. η SE is the energy consumption evaluation index of the current layer; η SW is the operation effect evaluation index of the current layer; E Sout is the effective energy output / kJ of the current layer to complete the set working conditions; E Sloss is the ineffective energy input / kJ of the current layer to complete the set working conditions; t S is the time / s spent by the current layer to complete the set working conditions.

[0026] Furthermore, the quantitative relationship approximation model between the energy efficiency evaluation indicators from the i-th layer to the (i - 1)-th layer in step 4 is expressed as follows:

[0027]

[0028] In the formula, y represents the design goal of the upper layer in adjacent hierarchies; x represents the relevant design variables of the lower layer in adjacent hierarchies; the function f is not only the bridge for strong non-linear mapping between hierarchies but also the core of information exchange; the superscript i represents a specific layer in the multi-level decomposition structure; m and n respectively represent the serial numbers of the design goals of the upper layer and the design variables of the lower layer in adjacent hierarchies with a subordinate relationship; while M and N respectively represent the total numbers of the design goals of the upper layer and the design variables of the lower layer in adjacent hierarchies with a subordinate relationship.

[0029] Furthermore, the solution calculation of the optimal feasible interval of the energy efficiency evaluation indicator for the (n + 1)-th layer using the single-objective two-level optimization algorithm in step 5 includes: constructing the mathematical model of the single-objective two-level model as follows:

[0030] The mathematical model of the single-objective two-level model is constructed as follows:

[0031]

[0032] In the formula, the design variable x = [x 1 , x 2 , …, x d T is a vector in the d-dimensional Euclidean space R d ; y is the objective function; f(x) is the hierarchical mapping function; h i (x) = 0 and g j (x) ≤ 0 are the equality constraint and the inequality constraint respectively; is the boundary condition that the objective function needs to satisfy; λ is the set confidence level; δ is the safety factor; P is a probabilistic operator defined as follows:

[0033]

[0034] Solving the single-objective two-level model includes: initially selecting the design variable interval, adjusting the feasible region of the design variables, setting the confidence level, and calculating the optimal feasible interval of the indicator at the confidence level.

[0035] Furthermore, the initial selection of the design variable interval includes: judging whether the initial value interval of the design variables has been determined. If so, adjust the feasible region of the design variables; if not, initially set the value range of the design variables according to engineering experience;

[0036] ​The set confidence level includes: calculating the confidence level, which is the percentage of the number of design variable combinations that meet the design objective in the specific design variable value range among all design variable combinations, and calculating the conflict level, which is the percentage of the number of design variable combinations that do not meet the design objective among all design variable combinations;

[0037] The optimal feasible interval of the index under the calculated confidence level includes: calculating the optimal interval of the design variable at the given confidence level. By combining the intelligent optimization algorithm and the interval search technology, the global optimal solution of the mathematical model can be first solved by using the intelligent optimization algorithm. On the basis of this optimal solution, the interval search method of the design variable is further applied to expand the value range where the optimal solution is located.

[0038] Further, step 8 includes: screening key components according to the sensitivity analysis results, determining the optimization object according to the screening results, and adjusting the parameters of the optimization object based on the optimal feasible interval of the energy efficiency evaluation index at the component level until the energy efficiency index at the whole machine level meets the standard.

[0039] Further, the screening of key components according to the sensitivity analysis results includes perturbing the simulation data of the lower level in the adjacent levels up and down and then inputting it into the approximate model of the quantitative relationship of the approximate model to obtain the sensitivity impact of the parameters of the lower level components in the adjacent levels on the energy efficiency performance of the higher level in the adjacent levels, and screening key components according to the sensitivity impact.

[0040] Further, step 7 includes: evaluating the energy efficiency of each level of the actual whole machine. If the energy efficiency index of a certain level falls within the optimal feasible interval of the energy efficiency index of the corresponding level, the energy efficiency evaluation is "meeting the energy efficiency requirements"; otherwise, the energy efficiency evaluation is "not meeting the energy efficiency requirements".

[0041] Further, the CAE model of the whole machine covers the multi-domain coupling and energy dissipation performance of the machine - electricity - hydraulics, and is calibrated and corrected by the experimental data of the actual typical working conditions of the target construction machinery; the experimental data includes the signal input situation, the dynamic actuation situation of the traveling device and the working device, the energy input and output situation of each component, and the energy consumption situation of the whole machine.

[0042] Further, step 1 includes: sorting out the transmission paths of various energies including electric energy, energy - recovered electric energy, mechanical energy, and hydraulic potential energy, etc. in the whole machine, and determining the basic energy - related units and energy transmission relationships; performing hierarchical decomposition according to the energy flow sorting situation to form an energy efficiency hierarchical decomposition architecture from the higher level to the lower level, namely the first layer, the second layer,..., the i - th layer. If i is 4, an energy efficiency hierarchical decomposition architecture of the vehicle level, the system level, the subsystem level, and the component level is formed; if i is 3, an energy efficiency hierarchical decomposition architecture of the whole machine level, the system level, and the component level is formed.

[0043] A method for improving the energy efficiency of a multi-level full system of construction machinery uses a method for evaluating the energy efficiency of a multi-level full system of construction machinery to obtain the optimal feasible intervals of the energy efficiency evaluation indicators at the whole machine level, the second level, …, the i-th level. If it is determined in step 7 that the actual energy efficiency of the whole machine level does not meet the standard, the actual component layer parameters are adjusted according to the optimal feasible intervals of the component layer energy efficiency evaluation indicators so that the energy efficiency of the whole machine level reaches the optimal feasible interval of the whole machine level energy efficiency evaluation indicators.

[0044] Further, key components are screened according to the sensitivity analysis results, the optimization objects are determined according to the screening results, and the parameters of the optimization objects are adjusted based on the optimal feasible intervals of the component layer energy efficiency evaluation indicators until the energy efficiency indicators of the whole machine level meet the standard.

[0045] Further, the screening of key components according to the sensitivity analysis results includes perturbing the simulation data of the lower level in the adjacent levels up and down and then inputting it into the approximate model of the quantitative relationship of the approximate model to obtain the sensitivity influence of the parameters of the lower level components in the adjacent levels on the energy efficiency performance of the higher level in the adjacent levels, and screening the key components according to the sensitivity influence.

[0046] The beneficial effects of the present invention include:

[0047] The present invention adopts an evaluation method integrating knowledge and data-driven. The "knowledge" is to build a construction machinery mechanical-electro-hydraulic coupling interaction model with high simulation degree that can meet the energy efficiency evaluation requirements, and the "data-driven" is to use the model simulation data to establish the direct quantitative relationship between the evaluation indicators at multiple scales (such as "whole machine", "system", "subsystem" and "component" levels).

[0048] The construction of a high-precision CAE model is carried out, which can obtain the system state parameters of the vehicle more comprehensively and evaluate the energy efficiency performance of the extra-large hybrid loader under different working conditions more effectively. This method only needs to collect experimental data of the actual typical working conditions of the target construction machinery at the initial stage of modeling for model verification and correction. After the model is built, only simulation is needed to obtain the state parameters of the vehicle, and there is no need to collect real-time data samples for a long time. Therefore, there is no need to equip a large number of instruments and equipment to monitor and record the state energy parameters in real time, which can effectively reduce the cost.

[0049] The present invention solves the problem of constantly modifying and updating the model under the operating parameters and working environment of construction machinery in different working conditions. Once the model is established, the model parameters and conditions can be quickly adjusted under different conditions subsequently without rebuilding the model. This greatly reduces the input of time and manpower and improves the generality of energy efficiency evaluation.

[0050] Regarding most engineering machinery energy evaluation systems, they only focus on low energy consumption while neglecting the crucial dimension of operation effectiveness. The present invention proposes the concept of "operation effectiveness" and implements an energy efficiency evaluation index for comprehensive evaluation from two dimensions of "energy consumption" and "operation effectiveness", avoiding the defect of excessive pursuit of low energy consumption in the prior art. For example, under high load or emergency working conditions, simply reducing energy consumption may lead to an extended operation time or a decline in equipment performance. However, by considering operation efficiency simultaneously, the present invention ensures that while saving energy, the operation ability can also be maintained or enhanced, guaranteeing the overall operation efficiency. At the same time, from the perspective of "energy consumption", the present invention also takes into account the influence of "ineffective energy consumption". Energy consumption includes two parts: "effective energy consumption" and "ineffective energy consumption", specifically fuel (power battery) consumption and tire wear consumption. During the vehicle's driving process, the friction between the tires and the ground will also cause energy loss. In the long run, "ineffective energy consumption" will also become an important part of energy consumption.

[0051] The present invention is a full-scale (at levels such as "whole machine", "system", "sub-system", and "components", etc.) evaluation method. It conducts simulations using the established CAE model, collects basic data according to the energy efficiency hierarchical framework, and constructs an approximate model of the quantitative relationship between multi-level (including "whole machine", "system", "sub-system", and "components", etc.) evaluation indicators. Since the present invention has multi-level evaluation indicators, there are complex interdependent relationships and multi-dimensional dimensions among them. Directly deriving their quantitative relationship has always been a challenge in the prior art. The present invention proposes to use the "multi-level strong non-linear target mapping" method in machine learning technology to solve this problem, not only improving the calculation speed but also ensuring the high reliability of the optimization result. Brief Description of the Drawings

[0052] Figure 1 It is a flowchart of a multi-level full-system energy efficiency evaluation method for engineering machinery according to an embodiment of the present application.

[0053] Figure 2 It is a schematic diagram of energy flow sorting according to an embodiment of the present application.

[0054] Figure 3 It is a framework of the whole machine energy efficiency hierarchical decomposition system according to an embodiment of the present application.

[0055] Figure 4 It is a numerical framework of the energy efficiency hierarchical decomposition system according to an embodiment of the present application.

[0056] Figure 5 It is a single-objective two-level decomposition model according to an embodiment of the present application.

[0057] Figure 6 It is a solution process of the single-objective two-level decomposition model according to an embodiment of the present application.

[0058] Figure 7 This is a flowchart of a multi - level full - system energy efficiency evaluation method for construction machinery according to an embodiment of the present application.

[0059] Figure 8 This is a flowchart of a multi - level full - system energy efficiency improvement method for construction machinery according to an embodiment of the present application. Detailed implementation manners

[0060] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Therefore, the detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the protection scope of the present application.

[0061] A multi - level full - system energy efficiency evaluation method for construction machinery, as Figure 1 shown, includes the following steps:

[0062] Step 1: Hierarchically decompose the whole machine according to the energy flow to obtain a multi - level energy efficiency hierarchical decomposition architecture. The multi - level includes the first layer, the second layer,..., the i - th layer from the high - level to the low - level, where the first layer is the whole - machine layer and the i - th layer is the component layer;

[0063] Step 2: Establish energy efficiency evaluation indicators for the whole - machine layer and determine the corresponding optimal feasible intervals, including energy consumption and operation effectiveness;

[0064] Step 3: Build a CAE model of the whole machine for simulation to obtain simulation data;

[0065] Step 4: Based on the simulation data, construct an approximate quantitative relationship model between the energy efficiency evaluation indicators from the n - th layer to the (n + 1) - th layer through a multi - level non - linear objective mapping, where n takes values of 1, 2,..., i - 1;

[0066] Step 5: Based on the optimal feasible intervals of the energy efficiency evaluation indicators of the n - th layer, calculate the optimal feasible intervals of the energy efficiency evaluation indicators of the (n + 1) - th layer for the approximate quantitative relationship model obtained in Step 4 through an intelligent optimization algorithm and an interval search method;

[0067] Step 6: Determine whether there are optimal feasible intervals for the energy efficiency evaluation indicators corresponding to each level. If yes, go to Step 7; otherwise, return to Step 4;

[0068] Step 7: Determine whether the actual energy efficiency of the whole machine layer, the second layer, …, the i-th layer meets the standard according to the optimal feasible intervals of the energy efficiency evaluation indicators of the whole machine layer, the second layer, …, the i-th layer;

[0069] Step 8: If it is determined in Step 7 that the actual energy efficiency of the whole machine layer does not meet the standard, then adjust the actual component layer parameters according to the optimal feasible intervals of the component layer energy efficiency evaluation indicators to make the energy efficiency of the whole machine layer reach the optimal feasible interval of the whole machine layer energy efficiency evaluation indicator.

[0070] In another embodiment, the formula for the energy efficiency evaluation indicator of the whole machine layer is:

[0071] η VEE =α 1 ·η VE +β 1 ·η VW

[0072]

[0073] Wherein, α 1 , β 1 represent the energy efficiency evaluation weights of the whole machine layer, and their sum is 1, η VE is the whole machine energy consumption evaluation indicator; η VW is the whole machine operation effect evaluation indicator; E Vout is the effective energy consumption / kJ of the whole machine to complete the set working condition; E Vloss is the ineffective energy consumption / kJ of the whole machine to complete the set working condition; M is the weight / t of the shoveled material of the whole machine to complete the set working condition; t V is the time / s spent by the whole machine to complete the set working condition;

[0074] The formula for the energy efficiency evaluation indicators of the second layer, …, the i-th layer is:

[0075] η SEE =α·η SE +β i ·η SW

[0076]

[0077] Wherein, α i , β i represent the energy efficiency evaluation weights of the i-th layer, and their sum is 1, η SE is the current layer energy consumption evaluation indicator; η SW is the current layer operation effect evaluation indicator; E Sout is the effective energy output / kJ of the current layer to complete the set working condition; E Sloss is the ineffective energy input / kJ of the current layer to complete the set working condition; t SThe time spent to complete the set working conditions for the current layer / s.

[0078] Specifically, the weight can be determined according to specific circumstances. For example, if a certain model values energy consumption more than operation during operation, then α is set higher.

[0079] The operation condition of the whole machine is characterized by the weight of the loaded goods. The characterization can be achieved by outputting how much energy (i.e., "the effective energy output of the current layer to complete the set working conditions") to the next link in the energy flow through this layer, so as to characterize the operation conditions of lower levels such as systems and subsystems.

[0080] In another embodiment, the quantitative relationship approximation model between the energy efficiency evaluation indicators from the i-th layer to the (i - 1)-th layer in step 4 is expressed as follows:

[0081]

[0082] In the formula, y represents the design objective of the higher layer in adjacent levels; x represents the relevant design variables of the lower layer in adjacent levels; the function f is not only the bridge for the strongly non-linear mapping between levels but also the core of information exchange; the superscript i represents a specific level in the multi-level decomposition structure; m and n respectively represent the serial numbers of the design objectives of the higher layer and the design variables of the lower layer in adjacent levels with a subordinate relationship; while M and N respectively represent the total numbers of the design objectives of the higher layer and the design variables of the lower layer in adjacent levels with a subordinate relationship.

[0083] In another embodiment, the solution calculation of the optimal feasible interval of the energy efficiency evaluation indicator for the (n + 1)-th layer by using the single-objective two-level optimization algorithm in step 5 includes: constructing the mathematical model of the single-objective two-level model, as Figure 5 shown:

[0084]

[0085] In the formula, the design variable x = [x 1 , x 2 , …, x d T is a vector in the d-dimensional Euclidean space R d ; y is the objective function; f(x) is the level mapping function; h i (x) = 0 and g j (x) ≤ 0 are the equality constraint and the inequality constraint respectively; is the boundary condition that the objective function needs to satisfy; λ is the set confidence level; δ is the safety factor; P is a probabilistic operator defined as follows:

[0086]

[0087] Solve the single-objective two-level model, as Figure 6As shown, it includes: the initial selection of the design variable range, adjusting the feasible region of the design variables, setting the confidence level, and calculating the optimal feasible range of the indicators at the confidence level.

[0088] In another embodiment, the initial selection of the design variable range includes: determining whether the initial value range of the design variables has been determined. If so, adjust the feasible region of the design variables; if not, preliminarily set the value range of the design variables based on engineering experience.

[0089] The setting of the confidence level includes: calculating the confidence level, that is, the percentage of the number of design variable combinations that meet the design objectives in all design variable combinations within a specific design variable value range, and calculating the conflict level, that is, the percentage of the number of design variable combinations that do not meet the design objectives in all design variable combinations.

[0090] The calculation of the optimal feasible range of the indicators at the confidence level includes: calculating the optimal range of the design variables at the given confidence level. By combining intelligent optimization algorithms and interval search techniques, the global optimal solution of the mathematical model can be first solved using the intelligent optimization algorithm. On the basis of this optimal solution, the interval search method of the design variables is further applied to expand the value range where the optimal solution is located.

[0091] In another embodiment, step 7 includes: performing energy efficiency evaluation on each level of the actual whole machine. If the energy efficiency index of a certain level falls within the optimal feasible range of the energy efficiency index of the corresponding level, the energy efficiency evaluation is "meeting the energy efficiency requirements"; otherwise, the energy efficiency evaluation is "not meeting the energy efficiency requirements".

[0092] In another embodiment, step 1 includes: sorting out the transfer paths of various types of energy, including electrical energy, recovered electrical energy, mechanical energy, and hydraulic potential energy, etc., in the whole machine, and determining the basic energy-related units and energy transfer relationships; performing hierarchical decomposition according to the energy flow sorting situation to form an energy efficiency hierarchical decomposition architecture from the high level to the low level, namely the first layer, the second layer,..., the i-th layer. When i is 4, an energy efficiency hierarchical decomposition architecture of the vehicle level, system level, subsystem level, and component level is formed. If i is 3, an energy efficiency hierarchical decomposition architecture of the whole machine level, system level, and component level is formed.

[0093] In another embodiment, taking a super-large hybrid loader as the research object, an energy efficiency evaluation method for multiple levels and the whole system of construction machinery is implemented, as Figure 7 shown, including the following steps:

[0094] Step 1.1: Use AMESIM and MATLAB / Simscape software to build a high-precision CAE model, including power source models such as battery packs and engines, electric drive system models such as generators and motors, hydraulic system models such as brake hydraulics, steering hydraulics, and working hydraulics, and a three-dimensional multi-body model of the loader vehicle. The built model can cover the multi-domain coupling and energy dissipation performance of the machine-electric-hydraulic system.

[0095] In this step, collect experimental data according to the actual typical working conditions of the target construction machinery. The data includes signal input conditions, dynamic actuation conditions of the traveling device and working device, energy input and output conditions of each component, and the energy consumption of the whole machine, etc. Specifically, it includes the fuel consumption, power consumption of the whole machine, input power of the electric drive system, output power of the electric drive system, input and output power of hydraulic systems such as brake hydraulics, steering hydraulics, and working hydraulics. At the same time, collect the driver's operation signals and input them into the simulation model to observe the dynamic performance of the model, complete the operation cycle time and the energy input and output of each part. On the premise of ensuring operation consistency, correct the energy dissipation of the model to ensure that the error of energy input and output of each part is less than 10%.

[0096] Step 1.2: Establish an energy efficiency evaluation index for the whole machine. In the field of construction machinery, not only the energy consumption situation needs to be concerned, but also the operation effectiveness needs to be considered. Therefore, the energy efficiency should be comprehensively evaluated from two dimensions of "energy consumption" and "operation effectiveness". According to the research situation, taking the loader as an example, the tire cost accounts for about 30% of the total usage cost. The TORO100DH loader consumes nearly 40 tires per year, and the service life of one tire is between 1 week and 1 month. Therefore, the energy efficiency evaluation index of the whole machine consists of two parts: energy consumption and operation effectiveness. "Energy consumption" includes two parts: "effective energy consumption" and "ineffective energy consumption", specifically fuel (power battery) consumption and tire wear consumption, while "operation effectiveness" includes two aspects: "operation volume" and "operation duration". Therefore, the energy efficiency index of the whole machine is established as follows:

[0097]

[0098] In the formula, Q 1 is the equivalent heat of fuel consumption and power consumption / kJ; Q 2 is the heat loss due to tire slip / kJ; m: the weight of the shoveled material to complete the set working condition / t; t: the time spent to complete the set working condition / s;

[0099] Step 2.1: Sort out the energy flow path of the whole machine. Taking Figure 2 as an example, sort out the transmission paths of energies such as electric energy, recovered electric energy, mechanical energy, and hydraulic potential energy in the hybrid distributed electric drive loader, so as to clarify the basic energy-related units and energy transmission relationships.

[0100] Step 2.2: Hierarchical multi-objective decomposition. Hierarchical decomposition is carried out according to the energy flow sorting to form an energy efficiency hierarchical decomposition architecture at the vehicle level, system level, subsystem level, and component level. In this embodiment, the systems and subsystems are divided according to different functions to form an overall vehicle energy efficiency hierarchical decomposition system framework as Figure 3 shown. In this embodiment, the system level is divided into a power source system, an electric drive system, and a hydraulic system. Among them, the power source system and the electric drive system have no subsystems below them. The hydraulic system has three subsystems below it, namely a braking system, a steering system, and an operation system. The power source system and the electric drive system directly reach the component level below. The component level under the power source system includes energy supply components such as power batteries and engines. The component level under the electric drive system includes components related to electric drive walking such as DC / DC, generators, and traction motors. For the subsystem level, the component level under the braking system includes brake cylinders, fans, and brake & fan pumps, etc. The component level under the steering system includes steering pumps, steering cylinders, etc. The component level under the steering system includes steering pumps, steering cylinders, etc. The component level under the operation system includes bucket cylinders, working pumps, boom cylinders, etc.

[0101] Step 3: Approximate model construction. Use the established CAE model to carry out simulations, and collect basic data under various working conditions according to the energy efficiency hierarchical framework. During the data collection process, the lower-level data can be perturbed up and down and then input into the approximate model. The purpose of doing this is, firstly, to ensure that the approximate model has a higher fitting accuracy for different component parameters and working conditions, and secondly, to obtain the sensitivity of the lower-level component parameters to the higher-level performance and even the overall vehicle energy efficiency performance, so as to facilitate subsequent rapid investigation and positive optimization work around the key parameters with higher sensitivity. These basic data need to cover the overall vehicle energy efficiency value, the energy output and efficiency indicators of each system and its subsystems, and the core design parameters of key components. Through these data, we can construct an approximate model of the quantitative relationship between multi-level (including "overall vehicle", "system", "subsystem", and "component", etc.) evaluation indicators.

[0102] In Figure 3 the energy efficiency hierarchical decomposition system architecture shown, the transfer relationships between the vehicle level, system level, subsystem level, component level, and between adjacent levels are expressed in mathematical form, and further Figure 4 the numerical architecture of the energy efficiency hierarchical decomposition system shown can be obtained, that is, the constructed approximate model.

[0103] In this step, to characterize the comprehensive contribution of each system to energy consumption and operation effectiveness, the system level and the subsystem select two indicators of "total output work" and "self-energy efficiency", and some design variables of key components are selected for the bottom-level components. Among them, the direct quantitative relationship between the design objectives of adjacent levels can be expressed as a functional relationship in the following form:

[0104]

[0105] Where y represents the high-level design goal between the upper and lower levels; x refers to the relevant design variables in the lower level; function f is not only a bridge for strong nonlinear mapping between levels, but also the core of information exchange; superscript i represents a specific level in the multi-level decomposition structure; m and n represent the serial numbers of adjacent high-level design goals and low-level design variables with subordinate relationships, respectively; and M and N represent the total number of design goals and design variables in these adjacent levels, respectively.

[0106] In this step, the determination of the function expression f is the key to whether the energy efficiency decomposition system is numerical or not. Since there are complex interdependencies and multi-dimensional dimensions between these multi-level evaluation indicators, it is very challenging to directly derive their quantitative relationships. Therefore, the "multi-level strong nonlinear target mapping" method in machine learning technology can be used to solve it, which can improve the calculation speed and ensure the high reliability of the optimization results.

[0107] In this embodiment, each layer is separated from the next layer by y m =f(x), y represents the design goal of the previous level, x represents the design variable parameter of the next level, and f is the above function expression. The number on the upper right corner of each letter represents the level. For example, the first This means that the target y of the first layer m , and some design variables selected in the second layer The relationship between 1 To express, f 1 It is a nonlinear function. Going down one level, we reach the relationship between the second-level design objectives and the third-level design variables, so in the second function expression, the superscript of y becomes 2 and the superscript of x becomes 3.

[0108] For the problem of components in the hierarchical decomposition method, the bottom layer should be implemented on the detailed parameters of the components, rather than referring to the components in general, because the foothold for optimizing energy efficiency through the adjustment of components is also the parameters of the components. In such an example, these component parameters are selected, and most of the variables are easy to understand, that is, the values ​​of these parameters. Some are not easy to understand due to the particularity of the vehicle model, specifically: generator working torque (the working torque of the generator of a general vehicle model is determined by the load, but the generator of this vehicle model works at a fixed torque, which is a set value), engine gear fixed speed (the engine speed of a general vehicle model is determined by the accelerator pedal, and the engine speed of this vehicle model is also a set value. The selection of the value is determined manually. After it is determined, the engine is set to work at this speed to output power).

[0109] Step 4: Multi-level evaluation for positive energy efficiency design. Based on the approximate model obtained in Step 3, calculate the optimal interval, which involves intelligent optimization algorithms and interval search methods. Through global optimization, the ranges of energy output values and energy efficiency of each system level and subsystem level can be obtained under the constraint of the target overall machine energy efficiency value, and the index ranges of the key parameters of each component can be obtained under the constraint of the energy output target value and energy efficiency target value of the upper level. Thus, the optimal feasible interval of the key parameters of each component can be obtained. Therefore, by comparing the actual overall machine experimental data with the optimal feasible interval of the indexes, it is possible to evaluate whether the energy efficiency of each actual component, subsystem level, and system level meets the standards.

[0110] A method for improving the multi-level full-system energy efficiency of construction machinery, as Figure 8 shown, adopts a multi-level full-system energy efficiency evaluation method for construction machinery to obtain the optimal feasible intervals of the energy efficiency evaluation indexes of the overall machine layer, the second layer,..., the i-th layer. If Step 7 determines that the energy efficiency of the actual overall machine layer does not meet the standards, then adjust the parameters of the actual component layer according to the optimal feasible interval of the energy efficiency evaluation indexes of the component layer to make the energy efficiency of the overall machine layer reach the optimal feasible interval of the energy efficiency evaluation indexes of the overall machine layer.

[0111] In another embodiment, screen key components according to the sensitivity analysis results, determine the optimization objects according to the screening results, and adjust the parameters of the optimization objects based on the optimal feasible interval of the energy efficiency evaluation indexes of the component layer until the energy efficiency indexes of the overall machine layer meet the standards.

[0112] In another embodiment, the screening of key components according to the sensitivity analysis results includes perturbing the simulation data of the lower level in the adjacent levels up and down and then inputting it into the approximate model of the quantitative relationship of the approximate model to obtain the sensitivity influence of the parameters of the lower level components in the adjacent levels on the energy efficiency performance of the higher level in the adjacent levels, and screening key components according to the sensitivity influence.

[0113] In another embodiment, taking an extra-large hybrid loader as the research object, after implementing a multi-level full-system energy efficiency evaluation method for construction machinery, since the hybrid power distributed electric drive loader is a complex mechanical-electrical-hydraulic coupling system, the influence of the design variables of each component on the overall energy efficiency performance is not the same. Therefore, to optimize the energy efficiency performance more quickly and targetedly, according to the sensitivity analysis results of the key component parameters that have a significant impact on energy efficiency obtained in step 3, the paths in the energy efficiency hierarchical decomposition framework can be selected, and one or more paths with higher sensitivity can be selected as the optimization targets. For other paths, once the relevant data are determined, they are set as fixed values. An optimization solution method is used for global optimization. Under the condition of artificially setting the feasible range of the design variables, with the overall energy efficiency performance of the machine as the optimization target, the selected paths are optimized, aiming to find the global optimal solution of these parameters in the energy efficiency model. After finding the optimal solution, starting from this solution and combining the confidence percentage as a constraint condition, by searching within the feasible interval of the design variables, this optimal solution is extended into an optimal feasible solution interval. The forward optimization design of the key component parameters is completed, and the overall energy efficiency is improved.

[0114] In this embodiment, the genetic algorithm is used as the optimization solution method.

[0115] The above-described embodiments only represent the specific implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation to the protection scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the technical solution of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application.

Claims

1. A multi-level full-system energy efficiency evaluation method for construction machinery, characterized in that: The following steps are involved: Step 1: Perform a hierarchical multi-objective decomposition of the whole machine according to the energy flow to obtain a multi-level energy efficiency hierarchical decomposition architecture, wherein the multi-levels include the first layer, the second layer, ..., the i-th layer from the high level to the low level, the first layer is the whole machine layer, and the i-th layer is the component layer; Step 2: Establish energy efficiency evaluation indicators for the entire machine layer and determine the corresponding optimal feasible range, wherein the energy efficiency evaluation indicators include energy consumption and operation effectiveness; Step 3: Build a CAE model of the whole machine for simulation and obtain simulation data; Step 4: Based on the simulation data, a quantitative relationship approximate model between the energy efficiency evaluation indicators of the nth layer to the n+1th layer is constructed through multi-level nonlinear target mapping, where n is 1, 2, …, i-1; Step 5: Based on the optimal feasible interval of the energy efficiency evaluation index of the nth layer, the optimal interval of the quantitative relationship approximation model obtained in step 4 is calculated by using the intelligent optimization algorithm and the interval search method to obtain the optimal feasible interval of the energy efficiency evaluation index of the n+1th layer; Step 6: Determine whether each level has the optimal feasible interval of the corresponding energy efficiency evaluation index. If yes, proceed to step 7, otherwise return to step 4; Step 7: Determine whether the actual energy efficiency of the whole machine layer, the second layer, ..., the i-th layer meets the standard according to the optimal feasible interval of the energy efficiency evaluation index of the whole machine layer, the second layer, ..., the i-th layer.

2. A method for evaluating the energy efficiency of a multi-level whole system of construction machinery according to claim 1, characterized in that: The formula for the energy efficiency evaluation index of the whole machine layer is: or VEE =α1·η VE +β1·η VW In the formula, α1 and β1 represent the energy efficiency evaluation weights of the whole machine layer, which add up to 1, and η VE is the energy consumption evaluation index of the whole machine; η VW It is the evaluation index of the whole machine operation effect; E Vout The effective energy consumption of the whole machine to complete the set working condition / kJ; E Vloss is the ineffective energy consumption of the whole machine to complete the set working condition / kJ; M is the weight of the shoveled material of the whole machine to complete the set working condition / t; t V The time it takes for the whole machine to complete the set working conditions / s; The formula for the energy efficiency evaluation index of the second layer, ..., the i-th layer is: or SEE =a i ·or SE +b i ·or SW In the formula, α i , β i represents the energy efficiency evaluation weight of the i-th layer, which adds up to 1, η SE is the energy consumption evaluation index of the current layer; η SW E is the evaluation index of the operation effect of the current layer; Sout The effective energy output of the current layer to complete the set working conditions / kJ; E Sloss Invalid energy input for the current layer to complete the set working condition / kJ; t S The time / s it takes to complete the set working condition for the current layer.

3. A multi-level full-system energy efficiency evaluation method for construction machinery according to claim 1, characterized in that: The quantitative relationship approximate model between the energy efficiency evaluation indicators of the i-th layer to the i-1-th layer in step 4 is expressed as follows: In the formula, y represents the design goal of the upper level in the adjacent level; x represents the relevant design variables of the lower level in the adjacent level; function f is not only a bridge for strong nonlinear mapping between levels, but also the core of information exchange; The superscript i indicates a specific level in the multi-level decomposition structure; m and n represent the serial numbers of high-level design objectives and low-level design variables in adjacent levels with subordinate relationships, respectively; M and N represent the total number of high-level design objectives and low-level design variables in adjacent levels with subordinate relationships, respectively.

4. The method for evaluating the energy efficiency of a multi-level whole system of construction machinery according to claim 1, characterized in that: In step 5, the single-objective dual-level optimization algorithm is used to solve and calculate the optimal feasible interval of the energy efficiency evaluation index of the n+1th layer, including: constructing a mathematical model of a single-objective dual-level model as follows: Where the design variable x=[x1,x2,…,x d ] T is a d-dimensional Euclidean space R d The vector in; y is the target function; f(x) is the hierarchical mapping function; h i (x) = 0 and g j (x)≤0 are equality constraints and inequality constraints respectively; y0 L ,y0 R is the boundary condition that the objective function needs to meet; λ is the confidence level set; δ is the insurance coefficient; P is a probabilistic operator defined as follows: Solving the single-objective two-level model includes: preliminarily selecting the design variable interval, adjusting the feasible domain of the design variable, setting the confidence level, and calculating the optimal feasible interval of the indicator under the confidence level.

5. A multi-level full-system energy efficiency evaluation method for construction machinery according to claim 4, characterized in that: The preliminary selection of the design variable interval includes: judging whether the initial value interval of the design variable has been determined, and if so, adjusting the feasible domain of the design variable; if not, preliminarily setting the value range of the design variable based on engineering experience; The setting of the confidence level includes: calculating the confidence level, i.e., the percentage of the number of design variable combinations that meet the design goal in the specific design variable value range to the number of all design variable combinations; calculating the conflict level, i.e., the percentage of the number of design variable combinations that do not meet the design goal to the number of all design variable combinations; The calculation of the optimal feasible interval of the indicator under the confidence level includes: calculating the optimal interval of the design variable under a given confidence level, and by combining the intelligent optimization algorithm and the interval search technology, the intelligent optimization algorithm can be first used to solve the global optimal solution of the mathematical model, and on the basis of this optimal solution, the interval search method of the design variable is further applied to expand the value interval where the optimal solution is located.

6. A multi-level full-system energy efficiency evaluation method for construction machinery according to claim 1, characterized in that: The CAE model of the whole machine covers the multi-domain coupling and energy dissipation performance of machine, electricity and fluid, and is verified and corrected by experimental data of actual typical working conditions of the target engineering machinery; the experimental data includes signal input conditions, dynamic actuation conditions of the walking device and the working device, energy input and output conditions of each component, and energy consumption conditions of the whole machine.

7. The method for evaluating the energy efficiency of a multi-level whole system of construction machinery according to claim 1, characterized in that: The step 1 includes: sorting out the transmission paths of various types of energy including electric energy, energy recovery electric energy, mechanical energy and hydraulic potential energy in the whole machine, determining the basic energy-related units and energy transfer relationships; performing hierarchical decomposition according to the energy flow sorting situation, forming a 1st layer, 2nd layer, ..., i-th layer energy efficiency hierarchical decomposition architecture from high level to low level.

8. A method for improving the energy efficiency of a multi-level system of engineering machinery, characterized in that: A multi-level full-system energy efficiency evaluation method for engineering machinery as described in any one of claims 1 to 7 is used to obtain the optimal feasible range of energy efficiency evaluation indicators of the whole machine layer, the second layer, ..., the i-th layer. If step 7 determines that the actual energy efficiency of the whole machine layer does not meet the standards, the actual component layer parameters are adjusted according to the optimal feasible range of the component layer energy efficiency evaluation indicators so that the energy efficiency of the whole machine layer reaches the optimal feasible range of the energy efficiency evaluation indicators of the whole machine layer.

9. A method for improving the energy efficiency of a multi-level whole system of engineering machinery according to claim 8, characterized in that: According to the results of sensitivity analysis, key components are screened, and the optimization object is determined based on the screening results. The parameters of the optimization object are adjusted based on the optimal feasible range of the energy efficiency evaluation index at the component level until the energy efficiency index of the whole machine level meets the standard.

10. A method for improving the energy efficiency of a multi-level whole system of engineering machinery according to claim 9, characterized in that: The method of screening key components based on the sensitivity analysis results includes perturbing the simulation data of the lower and middle levels of adjacent levels up and down and then inputting the data into the approximate model quantitative relationship approximation model to obtain the sensitivity of the parameters of the lower and middle levels of adjacent levels to the energy efficiency performance of the upper and middle levels of adjacent levels, and screening key components based on the sensitivity influence.

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