A method for evaluating and improving the energy efficiency of a multi-level, whole-system engineering machinery

Abstract

This invention discloses a method for multi-level, full-system energy efficiency evaluation and improvement of engineering machinery, relating to the field of energy efficiency evaluation technology. It addresses the technical problem that existing technologies cannot effectively perform multi-level energy efficiency assessments for complex systems with highly integrated mechanical, electrical, and hydraulic systems. The invention includes: hierarchical multi-objective decomposition of the entire machine based on energy flow to obtain a multi-level energy efficiency hierarchy; establishing energy efficiency evaluation indicators for the entire machine; building a CAE model of the entire machine for simulation and obtaining simulation data; constructing an approximate quantitative relationship model between the energy efficiency evaluation indicators of the nth to n+1th layers through multi-level nonlinear objective mapping based on the simulation data; obtaining the optimal feasible interval for the energy efficiency evaluation indicators of the n+1th layer based on the optimal feasible interval of the nth layer energy efficiency evaluation indicators; and determining whether the actual energy efficiency of each layer meets the standards based on the optimal feasible interval of each layer's energy efficiency evaluation indicators. This invention significantly reduces the investment of time and manpower and improves the versatility of energy efficiency evaluation.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency evaluation technology, specifically to a method for evaluating and improving the energy efficiency of a multi-level, full-system engineering machinery. Background Technology

[0002] Currently, in the traditional passenger vehicle sector, energy consumption indicators are mostly used to evaluate energy-saving effects. However, with energy efficiency becoming a focal point of competition in the construction machinery industry, evaluating it based on energy consumption indicators is far from effective in solving the energy efficiency evaluation problem. Therefore, the energy efficiency problem is how to maximize energy utilization and reduce emissions while ensuring operational effectiveness. This is the comprehensive reflection of energy consumption and operational effectiveness.

[0003] Existing technologies, such as the Chinese patent "Energy Efficiency Evaluation Method, Device and Storage Medium for Engineering Machinery Based on Digital Twin" (Patent No.: ZL202211072676.2), generate construction elements representing the entity of the construction object, generate a virtual body model through construction elements and operating parameters, compare the energy efficiency deviation, performance and fatigue of the entity and the virtual body to form an energy efficiency evaluation, and provide a device (processor and storage medium) for performing the energy efficiency evaluation method.

[0004] However, this patent is mainly aimed at the accumulation of large amounts of data. The accuracy of digital twin models is highly dependent on the quality and quantity of data. Therefore, high-precision measuring instruments are needed to collect data samples, which greatly increases the evaluation cost.

[0005] For example, the Chinese patent "A method for optimizing energy consumption of hybrid electric vehicles" (patent number: ZL202210129337.7) calculates the required torque of the wheels based on the vehicle's operating conditions and the remaining battery power; then, it uses a genetic algorithm to generate vehicle operating parameters in real time; and finally selects the individual with the smallest fitness value in the initial population, i.e. the individual with the smallest total fuel consumption, as the optimal individual through fitness calculation, and decodes and outputs the vehicle operating parameters corresponding to the optimal individual.

[0006] However, this patent selects the individual with the lowest fuel consumption as the optimal individual. It only considers energy consumption optimization and does not implement a comprehensive energy efficiency evaluation index that evaluates both "energy consumption" and "operational effectiveness".

[0007] For example, the Chinese patent "A Method and System for Energy Consumption Control of a Range-Extended Electric Loader" (Patent No.: ZL202211523918.5) obtains the system state parameters of the vehicle; it uses a recurrent neural network to predict the vehicle speed and drive motor power demand in the future time window; it uses the rolling optimization idea of ​​Model Predictive Control (MPC) and the particle swarm optimization algorithm to optimize the solution; it uses the Equivalent Minimum Energy Consumption Strategy (ECMS) algorithm to calculate the optimal output power combination sequence of the range extender and the battery pack; and it controls the power output of the range extender and the battery pack in the prediction window to achieve a low-energy consumption control effect.

[0008] However, the energy efficiency evaluation index of this patent is too simplistic. It uses the range extender power-optimal fuel consumption mapping model for optimization, but only considers fuel consumption or electricity consumption, without comprehensively considering the energy efficiency impact of other important systems. It does not achieve multi-level energy efficiency evaluation of the loader (including "whole machine", "system", "subsystem" and "components"). Summary of the Invention

[0009] To address the problems existing in the prior art, this invention provides a method for multi-level full-system energy efficiency evaluation and improvement of engineering machinery, solving the technical problem that the prior art cannot effectively conduct multi-level energy efficiency assessment for complex systems with high integration of mechanical, electrical, and hydraulic systems.

[0010] A method for evaluating the energy efficiency of a multi-level, full-system engineering machinery system includes the following steps:

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

[0012] Step 2: Establish energy efficiency evaluation indicators for the whole machine layer and determine the corresponding optimal feasible range. The energy efficiency evaluation indicators include energy consumption and operational effectiveness.

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

[0014] Step 4: Based on simulation data, construct an approximate quantitative relationship model between energy efficiency evaluation indicators from the nth layer to the (n+1th)th layer through multi-level nonlinear target mapping, where n takes the value of 1, 2, ..., i-1;

[0015] 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 using intelligent optimization algorithm and interval search method to obtain the optimal feasible interval of the energy efficiency evaluation index of the (n+1)th layer.

[0016] Step 6: Determine whether each level has an optimal feasible range for the corresponding energy efficiency evaluation index. If yes, proceed to Step 7; otherwise, return to Step 4.

[0017] Step 7: Determine whether the actual energy efficiency of the whole machine layer, the second layer, ..., the i-th layer meets the standard based on the optimal feasible range of the energy efficiency evaluation indicators of the whole machine layer, the second layer, ..., the i-th layer.

[0018] Furthermore, the formula for the overall energy efficiency evaluation index is as follows:

[0019] η VEE =α1·η VE +β1·η VW

[0020]

[0021] In the formula, α1 and β1 represent the energy efficiency evaluation weights of the entire system layer, and their sum is 1; η VE η is the energy consumption evaluation index for the entire machine. VW E is an evaluation index for the overall machine operation effect; Vout Effective energy consumption per kJ for the entire machine to complete the set operating conditions; E Vloss The ineffective energy consumption (kJ) for the whole machine to complete the set working conditions; M is the weight (t) of the shoveled material for the whole machine to complete the set working conditions; t V Time taken for the entire machine to complete the set operating conditions (in seconds);

[0022] The formulas for the energy efficiency evaluation indicators of the 2nd, ..., ith layers are as follows:

[0023] η SEE =α i ·η SE +β i ·η SW

[0024]

[0025] In the formula, α i β i This represents the energy efficiency evaluation weights for the i-th layer, which are summed to 1, η SE η is the energy consumption evaluation index for the current layer. SW E is the evaluation index for the current layer's work performance. Sout The effective energy output (kJ) for the current layer under the set operating conditions; E Sloss The invalid energy input / kJ for completing the set operating condition of the current layer; t S The time (in seconds) required for the current layer to complete the set working conditions.

[0026] Furthermore, the approximate model of the quantitative relationship between the energy efficiency evaluation indicators of 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 objectives of the higher levels in the adjacent hierarchy; x represents the relevant design variables of the lower levels in the adjacent hierarchy; the 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 ordinal numbers of the design objectives of the higher levels and the design variables of the lower levels in the adjacent hierarchy with subordinate relationships, respectively; while M and N represent the total number of the design objectives of the higher levels and the design variables of the lower levels in the adjacent hierarchy with subordinate relationships, respectively.

[0029] Furthermore, step 5, which uses a single-objective two-level optimization algorithm to solve for the optimal feasible interval of the (n+1)th level energy efficiency evaluation index, includes: constructing the mathematical model of the single-objective two-level model as follows:

[0030] The mathematical model for constructing a single-objective, two-level model is as follows:

[0031]

[0032] In the formula, the design variable x = [x1, x2, ..., x d ] T It is d-dimensional Euclidean space R d In the vector; y is the objective function; f(x) is the hierarchical mapping function; h i (x) = 0 and g j (x)≤0 represents the equality constraint and the inequality constraint, respectively; λ represents the boundary conditions that the objective function needs to satisfy; δ is the set confidence level; P is the insurance coefficient; P is a probabilistic operator defined as follows:

[0033]

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

[0035] Furthermore, the initial selection of design variable range includes: determining whether the initial value range of the design variables has been determined; if so, adjusting the feasible domain of the design variables; otherwise, based on engineering experience, initially setting the value range of the design variables.

[0036] The setting of confidence levels includes: calculating the confidence level, which is the percentage of design variable combinations that meet the design objectives within a specific range of design variable values, out of the total number of design variable combinations; and calculating the conflict level, which is the percentage of design variable combinations that do not meet the design objectives out of the total number of design variable combinations.

[0037] The calculation of the optimal feasible interval of the index at the confidence level includes: calculating the optimal interval of the design variable at a given confidence level. By combining intelligent optimization algorithm and interval search technology, the global optimal solution of the mathematical model can be solved first using the intelligent optimization algorithm. Based on this optimal solution, the interval search method of the design variable is further applied to expand and obtain the value interval of the optimal solution.

[0038] Furthermore, step 8 includes: screening key components based on sensitivity analysis results, determining optimization targets based on screening results, and adjusting the parameters of the optimization targets based on the optimal feasible range of the component-level energy efficiency evaluation index until the energy efficiency index of the whole machine meets the standard.

[0039] Furthermore, the step of screening key components based on sensitivity analysis results includes perturbing the simulation data of lower and middle levels in adjacent layers and then inputting it into the approximate model to quantitatively determine the approximate model of the relationship, so as to obtain the sensitivity influence of the parameters of lower and middle level components in adjacent layers on the energy efficiency performance of higher and middle levels in adjacent layers, and screening key components based on the sensitivity influence.

[0040] Furthermore, step 7 includes: performing energy efficiency evaluation on each level of the actual machine; if the energy efficiency index of a certain level falls within the optimal feasible range of the corresponding level's energy efficiency index, the energy efficiency evaluation is "meets energy efficiency requirements"; otherwise, the energy efficiency evaluation is "does not meet energy efficiency requirements".

[0041] Furthermore, the CAE model of the entire machine covers the multi-domain coupling and energy dissipation performance of mechanical-electrical-hydraulic systems, and is verified and corrected by experimental data from typical working conditions of the target engineering machinery. The experimental data includes signal input, dynamic operation of the walking and working devices, energy input and output of each component, and energy consumption of the entire machine.

[0042] Further, step 1 includes: sorting out the transmission paths of various types of energy, including electrical energy, energy recovery electrical energy, mechanical energy and hydraulic potential energy, in the whole machine, and determining the basic energy-related units and energy transmission relationships; performing hierarchical decomposition based on the energy flow sorting situation to form a layer 1, layer 2, ..., layer i of energy efficiency hierarchical decomposition architecture from high level to low level. If i is 4, an energy efficiency hierarchical decomposition architecture of whole vehicle level, system level, subsystem level, and component level is formed. If i is 3, an energy efficiency hierarchical decomposition architecture of whole machine level, system level, and component level is formed.

[0043] A method for improving the energy efficiency of a multi-level whole system of construction machinery is proposed. The method uses a multi-level whole system energy efficiency evaluation method to obtain the optimal feasible range of energy efficiency evaluation indicators for the whole machine level, the second level, ..., the i-th level. If step 7 determines that the actual energy efficiency of the whole machine level does not meet the standard, the parameters of the actual component level are adjusted according to the optimal feasible range of the component level energy efficiency evaluation indicators so that the energy efficiency of the whole machine level reaches the optimal feasible range of the whole machine level energy efficiency evaluation indicators.

[0044] Furthermore, key components are screened based on the sensitivity analysis results, optimization targets are determined based on the screening results, and the parameters of the optimization targets are adjusted based on the optimal feasible range of the component-level energy efficiency evaluation indicators until the energy efficiency indicators of the whole machine meet the standards.

[0045] Furthermore, the step of screening key components based on sensitivity analysis results includes perturbing the simulation data of lower and middle levels in adjacent layers and then inputting it into the approximate model to quantitatively determine the approximate model of the relationship, so as to obtain the sensitivity influence of the parameters of lower and middle level components in adjacent layers on the energy efficiency performance of higher and middle levels in adjacent layers, and screening key components based on the sensitivity influence.

[0046] The beneficial effects of this invention include:

[0047] This invention adopts a knowledge-data driven fusion evaluation method. "Knowledge" refers to building a mechanical-electrical-hydraulic coupling interaction model of engineering machinery with high simulation degree that can meet the needs of energy efficiency evaluation. "Data-driven" refers to using model simulation data to establish direct quantitative relationships between multi-scale ("whole machine", "system", "subsystem" and "component" levels) evaluation indicators.

[0048] A high-precision CAE model was built, enabling a more comprehensive acquisition of the vehicle's system state parameters and a more effective evaluation of the energy efficiency performance of extra-large hybrid loaders under different operating conditions. This method only requires experimental data collection on typical actual operating conditions of the target construction machinery in the early stages of modeling for model verification and correction. After the model is built, simulation is all that is needed to obtain the vehicle's state parameters, eliminating the need for long-term real-time data collection. Therefore, it eliminates the need for extensive equipment to monitor and record state parameters in real time, effectively reducing costs.

[0049] This invention solves the problem of constantly modifying and updating models for the operating parameters and working environments of construction machinery under different conditions. Once the model is established, its parameters and conditions can be quickly adjusted under different conditions without the need to rebuild the model. This greatly reduces the investment of time and manpower and improves the versatility of energy efficiency evaluation.

[0050] Most energy efficiency assessment systems for construction machinery focus solely on low energy consumption, neglecting the crucial dimension of operational effectiveness. This invention proposes the concept of "operational effectiveness," implementing a comprehensive energy efficiency evaluation index that considers both "energy consumption" and "operational effectiveness," thus avoiding the overemphasis on low energy consumption found in existing technologies. For example, under high load or emergency conditions, simply reducing energy consumption may lead to longer operating times or decreased equipment performance. This invention, by simultaneously considering operational efficiency, ensures that while saving energy, it also maintains or improves operational capabilities, guaranteeing overall operational efficiency. Furthermore, this invention considers the impact of "ineffective energy consumption" from the perspective of "energy consumption." Energy consumption includes both "effective energy consumption" and "ineffective energy consumption," specifically fuel (power battery) consumption and tire wear. During vehicle operation, friction between tires and the ground also causes energy loss; in the long run, "ineffective energy consumption" becomes a significant component of overall energy consumption.

[0051] This invention presents a comprehensive evaluation method (at the levels of "whole machine," "system," "subsystem," and "components"). Simulations are conducted using a pre-built CAE model, and basic data is collected based on a hierarchical energy efficiency framework. This leads to the construction of an approximate quantitative relationship model among multi-level evaluation indicators (including "whole machine," "system," "subsystem," and "components"). Because this invention involves multi-level evaluation indicators with complex interdependencies and multi-dimensional dimensions, directly deriving their quantitative relationships has been a challenge in existing technologies. This invention proposes a "multi-level strongly nonlinear target mapping" method from machine learning to solve this problem, improving computational speed and ensuring high reliability of the optimization results. Attached Figure Description

[0052] Figure 1 This is a flowchart of a multi-level, full-system energy efficiency evaluation method for engineering machinery, as described in an embodiment of this application.

[0053] Figure 2 This is a schematic diagram of the energy flow management involved in the embodiments of this application.

[0054] Figure 3 This is the framework of the overall energy efficiency hierarchy decomposition system involved in the embodiments of this application.

[0055] Figure 4 This is the numerical framework for the energy efficiency hierarchy decomposition system involved in the embodiments of this application.

[0056] Figure 5 This is the single-objective, two-level decomposition model involved in the embodiments of this application.

[0057] Figure 6 This describes the solution process for the single-objective, two-level decomposition model involved in the embodiments of this application.

[0058] Figure 7 This is a flowchart of a multi-level, full-system energy efficiency evaluation method for engineering machinery, as described in an embodiment of this application.

[0059] Figure 8 This is a flowchart of a multi-level, full-system energy efficiency improvement method for engineering machinery, as described in an embodiment of this application. Detailed Implementation

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

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

[0062] Step 1: Perform hierarchical multi-objective decomposition of the whole machine according to the energy flow to obtain a multi-level energy efficiency hierarchical decomposition architecture. The multi-level includes the 1st layer, the 2nd layer, ..., the ith layer from the high level to the low level, where the 1st layer is the whole machine layer and the ith layer is the component layer.

[0063] Step 2: Establish energy efficiency evaluation indicators for the entire machine layer and determine the corresponding optimal feasible range, including energy consumption and operational effectiveness;

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

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

[0066] 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 using intelligent optimization algorithm and interval search method to obtain the optimal feasible interval of the energy efficiency evaluation index of the (n+1)th layer.

[0067] Step 6: Determine whether each level has an optimal feasible range for the corresponding energy efficiency evaluation index. If yes, proceed 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 based on the optimal feasible interval of the energy efficiency evaluation index of the whole machine layer, the second layer, ..., the i-th layer.

[0069] Step 8: If Step 7 determines that the actual energy efficiency of the whole machine layer does not meet the standard, then adjust the parameters of the actual component layer according to the optimal feasible range of the component layer energy efficiency evaluation index so that the energy efficiency of the whole machine layer reaches the optimal feasible range of the whole machine layer energy efficiency evaluation index.

[0070] In another embodiment, the formula for the overall energy efficiency evaluation index is:

[0071] η VEE =α1·η VE +β1·η VW

[0072]

[0073] In the formula, α1 and β1 represent the energy efficiency evaluation weights of the entire system layer, and their sum is 1; η VE η is an indicator for evaluating the overall energy consumption of the machine. VW E is an evaluation index for the overall machine operation effect; Vout Effective energy consumption per kJ for the entire machine to complete the set operating conditions; E Vloss The ineffective energy consumption (kJ) for the whole machine to complete the set working conditions; M is the weight (t) of the shoveled material for the whole machine to complete the set working conditions; t V The time (in seconds) for the entire machine to complete the set operating conditions;

[0074] The formulas for the energy efficiency evaluation indicators of the 2nd, ..., ith layers are as follows:

[0075] η SEE =α·η SE +β i ·η SW

[0076]

[0077] In the formula, α i β i This represents the energy efficiency evaluation weights for the i-th layer, which are summed to 1, η SE η is the energy consumption evaluation index for the current layer. SW E is the evaluation index for the current layer's work performance. Sout The effective energy output (kJ) for the current layer under the set operating conditions; E Sloss The invalid energy input / kJ for completing the set operating condition of the current layer; t S The time (in seconds) required for the current layer to complete the set working conditions.

[0078] Specifically, the weighting can be determined by the specific circumstances. For example, if a certain model prioritizes energy consumption over operation, then α should be set higher.

[0079] The overall machine operation is characterized by the weight of the load. The operation of lower levels, such as systems and subsystems, can be characterized by how much energy is output to the next stage of energy flow through this layer (i.e., "effective energy output for the current layer to complete the set operating conditions").

[0080] In another embodiment, the approximate model of the quantitative relationship between the energy efficiency evaluation indicators of 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 objectives of the higher levels in the adjacent hierarchy; x represents the relevant design variables of the lower levels in the adjacent hierarchy; the 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 ordinal numbers of the design objectives of the higher levels and the design variables of the lower levels in the adjacent hierarchy with subordinate relationships, respectively; while M and N represent the total number of the design objectives of the higher levels and the design variables of the lower levels in the adjacent hierarchy with subordinate relationships, respectively.

[0083] In another embodiment, step 5, which uses a single-objective two-level optimization algorithm to solve for the optimal feasible interval of the (n+1)th level energy efficiency evaluation index, includes: constructing a mathematical model of the single-objective two-level model, such as... Figure 5 As shown:

[0084]

[0085] In the formula, the design variable x = [x1, x2, ..., x d ] T It is d-dimensional Euclidean space R d In the vector; y is the objective function; f(x) is the hierarchical mapping function; h i (x) = 0 and g j (x)≤0 represents the equality constraint and the inequality constraint, respectively; λ represents the boundary conditions that the objective function needs to satisfy; δ is the set confidence level; P is the insurance coefficient; P is a probabilistic operator defined as follows:

[0086]

[0087] Solving for a single-objective, two-level model, such as Figure 6 As shown, the process includes: initially selecting the design variable range, adjusting the feasible region of the design variables, setting the confidence level, and calculating the optimal feasible range of the index under the confidence level.

[0088] In another embodiment, the initial selection of design variable range includes: determining whether the initial value range of the design variables has been determined; if so, adjusting the feasible domain of the design variables; otherwise, based on engineering experience, initially setting the value range of the design variables.

[0089] The setting of confidence levels includes: calculating the confidence level, which is the percentage of design variable combinations that meet the design objectives within a specific range of design variable values, out of the total number of design variable combinations; and calculating the conflict level, which is the percentage of design variable combinations that do not meet the design objectives out of the total number of design variable combinations.

[0090] The calculation of the optimal feasible interval of the index at the confidence level includes: calculating the optimal interval of the design variable at a given confidence level. By combining intelligent optimization algorithm and interval search technology, the global optimal solution of the mathematical model can be solved first using the intelligent optimization algorithm. Based on this optimal solution, the interval search method of the design variable is further applied to expand and obtain the value interval of the optimal solution.

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

[0092] In another embodiment, step 1 includes: sorting out the transmission paths of various types of energy, including electrical energy, energy recovery electrical energy, mechanical energy and hydraulic potential energy, in the whole machine, and determining the basic energy-related units and energy transmission relationships; performing hierarchical decomposition based on the energy flow sorting situation to form a layer 1, layer 2, ..., layer i of energy efficiency hierarchical decomposition architecture from high level to low level. If i is 4, an energy efficiency hierarchical decomposition architecture of whole vehicle level, system level, subsystem level, and component level is formed. If i is 3, an energy efficiency hierarchical decomposition architecture of whole machine level, system level, and component level is formed.

[0093] In another embodiment, a super-large hybrid loader is used as the research object, and a multi-level, full-system energy efficiency evaluation method for engineering machinery is implemented, such as... Figure 7 As shown, it includes 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 braking hydraulic, steering hydraulic, and working hydraulic, as well as a three-dimensional multi-body model of the loader vehicle. The built model can cover the multi-domain coupling of mechanical, electrical and hydraulic systems and energy dissipation.

[0095] In this step, experimental data is collected based on the actual typical working conditions of the target construction machinery. The data includes signal input, dynamic operation of the walking and working devices, energy input and output of each component, and overall energy consumption. Specifically, this includes overall fuel consumption, electricity consumption, input power of the electric drive system, output power of the electric drive system, input and output power of hydraulic systems such as braking hydraulic, steering hydraulic, and working hydraulic. At the same time, the driver's operation signals are collected and input into the simulation model to observe the model's dynamic performance, the completion of the work cycle time, and the energy input and output of each part. Under the premise of ensuring operational consistency, the energy dissipation of the model is corrected to ensure that the error of energy input and output of each part is less than 10%.

[0096] Step 1.2: Establish overall machine energy efficiency evaluation indicators. In the construction machinery field, it's crucial to consider not only energy consumption but also operational effectiveness. Therefore, energy efficiency must be comprehensively evaluated from two dimensions: "energy consumption" and "operational effectiveness." Based on research, taking loaders as an example, tire costs account for approximately 30% of total operating costs. A TORO100DH loader consumes nearly 40 tires annually, with each tire having a lifespan of one week to one month. Therefore, the overall machine energy efficiency evaluation indicators consist of two parts: energy consumption and operational effectiveness. "Energy consumption" includes "effective energy consumption" and "ineffective energy consumption," specifically fuel (power battery) consumption and tire wear. "Operational effectiveness" includes "work volume" and "work duration." Therefore, the overall machine energy efficiency indicators are established as follows:

[0097]

[0098] In the formula, Q1 is the equivalent heat of fuel consumption and electricity consumption / kJ; Q2 is the heat loss due to tire slippage / kJ; m: the weight of the shoveled material to complete the set working condition / t tons; t: the time taken to complete the set working condition / s;

[0099] Step 2.1: Overall Energy Flow Path Analysis. (Based on...) Figure 2 Taking this as an example, we can analyze the transmission paths of various energies, such as electrical energy, energy recovery energy, mechanical energy, and hydraulic potential energy, in a hybrid distributed electric drive loader, thereby clarifying the basic energy-related units and energy transmission relationships.

[0100] Step 2.2: Hierarchical Multi-Objective Decomposition. Based on the energy flow analysis, a hierarchical decomposition is performed 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 their functions, forming a vehicle-wide energy efficiency hierarchical decomposition system framework as follows: Figure 3As shown, in this embodiment, the system level is divided into a power source system, an electric drive system, and a hydraulic system. The power source system and electric drive system have no subsystems. The hydraulic system has three subsystems: a braking system, a steering system, and a working system. The power source system and electric drive system are directly categorized down to the component level. The power source system's component level includes power supply components such as the power battery and engine. The electric drive system includes components related to electric drive, such as DC / DC converters, generators, and traction motors. At the subsystem level, the braking system's component level includes brake cylinders, fans, and brake & fan pumps, etc. The steering system's component level includes steering pumps and steering cylinders, etc. The working system's component level includes bucket cylinders, work pumps, boom cylinders, etc.

[0101] Step 3: Approximate Model Construction. Simulations are conducted using the established CAE model. Based on the hierarchical energy efficiency framework, basic data is collected under various operating conditions. During data collection, lower-level data can be perturbed before being input into the approximate model. This serves two purposes: first, to ensure the approximate model has higher fitting accuracy for different component parameters and operating conditions; and second, to obtain the sensitivity of lower-level component parameters to higher-level performance and even overall system energy efficiency, facilitating subsequent rapid troubleshooting and forward optimization around key parameters with high sensitivity. This basic data needs to cover the overall system energy efficiency value, the energy output and efficiency indicators of each system and its subsystems, and the core design parameters of key components. Through this data, we can construct a quantitative approximate model of the relationships between multi-level evaluation indicators (including "system", "subsystem", and "components").

[0102] exist Figure 3 In the energy efficiency hierarchy decomposition system framework shown, the transmission relationships between the vehicle level, system level, subsystem level, component level, and adjacent levels are expressed mathematically, which can be further obtained. Figure 4 The numerical framework of the energy efficiency hierarchy decomposition system shown is the approximate model that has been constructed.

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

[0104]

[0105] In the formula, y represents the high-level design goal between upper and lower levels; x refers to the relevant design variables in the lower level; the 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 ordinal 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, determining the function expression f is crucial for quantifying the energy efficiency decomposition system. Because these multi-level evaluation indicators have complex interdependencies and multi-dimensional dimensions, directly deriving their quantitative relationships is very challenging. Therefore, the "multi-level strongly nonlinear target mapping" method from machine learning techniques can be used to solve this problem, which can improve computational speed and ensure high reliability of the optimization results.

[0107] In this embodiment, each layer and the next layer are separated by a y-axis. m =f(x), where y represents the design goal of the previous level, x represents the design variable parameters of the next level, and f is the function expression described above. The superscript number of each letter indicates the level. For example, the first... It means the target y of the first layer m and some design variables selected in the second layer The relationship between them is expressed using f1, which is a non-linear function. Moving down another level, we arrive at the relationship between the second-level design objectives and the third-level design variables. Therefore, in the second function expression, the superscript of y is 2, and the superscript of x is 3.

[0108] Regarding the component issues in the hierarchical decomposition method, the bottom layer should focus on the detailed parameters of the components, rather than referring to them in general terms. This is because optimizing energy efficiency through component adjustments ultimately hinges on the parameters of those components. In this example, most of the selected component parameters are easy to understand; it's the values ​​of these parameters that are difficult to comprehend. Some are due to the specific characteristics of the vehicle model. Specifically: alternator operating torque (in general vehicles, the alternator operating torque is determined by the load, but in this model, the alternator operates at a constant torque, which is a pre-set value), and engine gear-specific speed (in general vehicles, the engine speed is determined by the accelerator pedal, but in this model, the engine speed is also a pre-set value, determined manually, and once determined, the engine operates 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, optimal interval calculation is performed. This step involves intelligent optimization algorithms and interval search methods. Through global optimization, the range of energy output values ​​and energy efficiency at each system level and subsystem level can be obtained under the constraint of the target overall energy efficiency value. Furthermore, the range of key parameters for each component can be obtained under the constraint of the target energy output value and energy efficiency target value at the next higher level. Thus, the optimal feasible interval for the key parameters of each component can be obtained. By comparing the actual overall system experimental data with the optimal feasible interval, the actual energy efficiency of each component, subsystem, and system level can be evaluated to determine whether it meets the standards.

[0110] A method for improving the energy efficiency of a multi-level, whole-system engineering machinery, such as Figure 8 As shown, a multi-level whole-system energy efficiency evaluation method for engineering machinery is adopted to obtain the optimal feasible range of energy efficiency evaluation indicators for 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 standard, the parameters of the actual component layer 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 whole machine layer energy efficiency evaluation indicators.

[0111] In another embodiment, key components are screened based on the sensitivity analysis results, optimization targets are determined based on the screening results, and the parameters of the optimization targets are adjusted based on the optimal feasible range of the component-level energy efficiency evaluation index until the energy efficiency index of the whole machine meets the standard.

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

[0113] In another embodiment, taking a super-large hybrid loader as the research object, after implementing a multi-level full-system energy efficiency evaluation method for engineering machinery, since the hybrid distributed electric drive loader is a complex electromechanical-hydraulic coupled system, the design variables of each component have different effects on the overall energy efficiency performance. Therefore, in order to optimize energy efficiency performance more quickly and effectively, based on the sensitivity analysis results of key component parameters that significantly affect energy efficiency obtained in step 3, the paths in the energy efficiency hierarchical decomposition framework can be carefully selected, and one or more paths with higher sensitivity can be chosen as optimization targets. For other paths, once the relevant data is determined, they are set to fixed values. A global optimization process is then performed using an optimization solution method. Under the condition of artificially setting the feasible range of design variables, the selected paths are optimized with the overall energy efficiency performance as the optimization target, with the aim of finding the global optimal solution for these parameters in the energy efficiency model. After finding the optimal solution, this solution is used as a starting point, and combined with the confidence percentage as a constraint, by searching within the feasible interval of the design variables, this optimal solution is expanded into an optimal feasible solution interval. Complete the forward optimization design of key component parameters to improve the overall energy efficiency of the machine.

[0114] This embodiment uses a genetic algorithm as the optimization solution method.

[0115] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. A multi-level, full-system energy efficiency evaluation method for engineering machinery, characterized in that, Includes the following steps: Step 1: Perform hierarchical multi-objective decomposition of the whole machine according to the energy flow to obtain a multi-level energy efficiency hierarchical decomposition architecture. The multi-level includes the 1st layer, the 2nd layer, ..., the ith layer from the high level to the low level. The 1st layer is the whole machine layer and the ith layer is the component layer. Step 2: Establish energy efficiency evaluation indicators for the whole machine layer and determine the corresponding optimal feasible range. The energy efficiency evaluation indicators include energy consumption and operational effectiveness. Step 3: Build a CAE model of the entire machine for simulation and obtain simulation data; Step 4: Based on simulation data, construct an approximate quantitative relationship model between energy efficiency evaluation indicators from the nth layer to the (n+1th)th layer through multi-level nonlinear target mapping, where n takes the value of 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 using intelligent optimization algorithm and interval search method to obtain the optimal feasible interval of the energy efficiency evaluation index of the (n+1)th layer. Step 6: Determine whether each level has an optimal feasible range for 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 based on the optimal feasible interval of the energy efficiency evaluation index of the whole machine layer, the second layer, ..., the i-th layer. Step 5 employs a single-objective, two-level optimization algorithm to calculate the optimal feasible interval for the (n+1)th level energy efficiency evaluation index, including: constructing the mathematical model of the single-objective, two-level model as follows: ； Design variables in the formula It is a d-dimensional European space Vectors in; It is the objective function; This is a hierarchical mapping function; These are equality constraints and inequality constraints, respectively. These are the boundary conditions that the objective function needs to satisfy; The set confidence level; This is the insurance factor; It is a probabilistic operator defined as follows: ； Solving a single-objective, two-level model includes: initially selecting the design variable range, adjusting the feasible region of the design variables, setting the confidence level, and calculating the optimal feasible range of the index under the confidence level. The CAE model of the whole machine covers the multi-domain coupling of mechanical, electrical and hydraulic systems and energy dissipation performance, and is verified and corrected based on experimental data of the actual typical working conditions of the target engineering machinery; the experimental data includes signal input, dynamic operation of the walking device and working device, energy input and output of each component, and energy consumption of the whole machine. Step 1 includes: sorting out the transmission paths of various types of energy, including electrical energy, energy recovery electrical energy, mechanical energy and hydraulic potential energy in the whole machine, and determining the basic energy-related units and energy transmission relationships; performing hierarchical decomposition based on the energy flow sorting situation to form a hierarchical energy efficiency decomposition architecture from high level to low level, namely the first level, the second level, ..., the i-th level.

2. The method for evaluating the energy efficiency of a multi-level, full-system engineering machinery according to claim 1, characterized in that, The formula for the overall energy efficiency evaluation index is as follows: ； In the formula, This represents the energy efficiency evaluation weights for the entire system layer, and they are summed to 1. As an evaluation index for the overall energy consumption of the machine; As an evaluation index for the overall machine operation effect; Effective energy consumption per kJ for the entire machine to complete the set operating conditions; Ineffective energy consumption (kJ) for the whole machine to complete the set operating conditions; The weight (t) of the material loaded by the machine to complete the set working conditions; Time taken for the entire machine to complete the set operating conditions (in seconds); The formulas for the energy efficiency evaluation indicators of the 2nd, ..., ith layers are as follows: ； In the formula, This represents the energy efficiency evaluation weights for the i-th layer, which sum to 1. This serves as an evaluation indicator for the energy consumption of the current layer. This serves as an evaluation indicator for the current layer's operational effectiveness. The effective energy output / kJ for the current layer under the set operating conditions; The invalid energy input / kJ for the current layer to complete the set operating conditions; The time (in seconds) required for the current layer to complete the set working conditions.

3. The method for evaluating the energy efficiency of a multi-level, full-system engineering machinery according to claim 1, characterized in that, The approximate model representing the quantitative relationship between the energy efficiency evaluation indicators of the i-th layer to the (i-1)-th layer in step 4 is as follows: ； In the formula, This represents the design goals of the middle and upper levels of adjacent floors; Represents the relevant design variables of the lower levels in adjacent layers; function It is not only a bridge for strongly nonlinear mappings between levels, but also the core of information exchange; superscript This indicates a specific level in a multi-level decomposition structure; and These represent the sequence numbers of higher-level design objectives and lower-level design variables in adjacent hierarchical levels with a subordinate relationship; while and These represent the total number of high-level design objectives and low-level design variables in adjacent hierarchical levels that have a subordinate relationship.

4. The method for evaluating the energy efficiency of a multi-level, full-system engineering machinery according to claim 1, characterized in that, The initial selection of design variable ranges includes: determining whether the initial value range of the design variables has been determined; if so, adjusting the feasible domain of the design variables; otherwise, based on engineering experience, initially setting the value range of the design variables. The setting of confidence levels includes: calculating the confidence level, which is the percentage of design variable combinations that meet the design objectives within a specific range of design variable values, out of the total number of design variable combinations; and calculating the conflict level, which is the percentage of design variable combinations that do not meet the design objectives out of the total number of design variable combinations. The calculation of the optimal feasible interval of the index at the confidence level includes: calculating the optimal interval of the design variable at a given confidence level. By combining intelligent optimization algorithm and interval search technology, the intelligent optimization algorithm is first used to solve the global optimal solution of the mathematical model. Based on this optimal solution, the interval search method of the design variable is applied to expand and obtain the value interval of the optimal solution.

5. A method for improving the energy efficiency of a multi-level, whole-system engineering machinery, characterized in that, The optimal feasible range of energy efficiency evaluation indicators for the whole machine layer, the second layer, ..., the i-th layer is obtained by using the multi-level whole system energy efficiency evaluation method of any one of claims 1-4. If step 7 determines that the actual energy efficiency of the whole machine layer does not meet the standard, the parameters of the actual component layer 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 whole machine layer energy efficiency evaluation indicators.

6. The method for improving the energy efficiency of a multi-level, whole-system engineering machinery according to claim 5, characterized in that, Based on the sensitivity analysis results, key components are screened, and optimization targets are determined based on the screening results. The parameters of the optimization targets are adjusted based on the optimal feasible range of the component-level energy efficiency evaluation indicators until the energy efficiency indicators of the whole machine meet the standards.

7. The method for improving the energy efficiency of a multi-level, whole-system engineering machinery according to claim 6, characterized in that, The process of selecting key components based on sensitivity analysis results involves perturbing the simulation data of lower and middle levels in adjacent layers and then inputting it into an approximate model to quantitatively determine the approximate relationship model. This process yields the sensitivity impact of the parameters of lower and middle level components in adjacent layers on the energy efficiency performance of higher and middle levels in adjacent layers. Key components are then selected based on this sensitivity impact.

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