A method for evaluating and analyzing energy efficiency of a hydraulic system of a distributed drive excavator

Abstract

The application discloses a kind of energy efficiency evaluation and analysis method of distributed drive excavator hydraulic system, obtain the multi-source heterogeneous data of distributed drive excavator hydraulic system under each working condition;According to the multi-source heterogeneous data, construct the electrical-hydraulic-mechanical multi-energy flow topology graph, according to the electrical-hydraulic-mechanical multi-energy flow topology graph, build the whole machine energy flow system;According to the whole machine energy flow system, construct three-dimensional evaluation matrix, according to three-dimensional evaluation matrix, energy efficiency anomaly tracing and energy efficiency report output are carried out.The application realizes the fine energy efficiency evaluation of distributed drive excavator multi-power source, multiple energy transmission path, multiple working condition, can identify efficiency bottleneck, quantify the influence of power coordination on whole machine energy consumption, evaluate energy recovery efficiency, and can assist in locating energy efficiency decline reason, provide scientific basis for energy efficiency optimization and energy management strategy design of distributed drive excavator.

Description

Technical Field

[0001] This invention relates to a method for evaluating and analyzing the energy efficiency of a distributed drive excavator hydraulic system, belonging to the field of energy efficiency evaluation technology for construction machinery. Background Technology

[0002] Existing energy efficiency evaluation methods are primarily designed for centralized power architectures and cannot adapt to the characteristics of independent control of multiple power sources in distributed drive systems. Specific problems include:

[0003] First, there is a lack of evaluation methods for energy interaction and power coupling between multiple power sources.

[0004] Second, it is impossible to quantify the efficiency of each link in the electro-hydraulic-mechanical multi-domain energy transfer link.

[0005] Third, existing methods do not consider the assessment of energy recovery and reuse efficiency unique to distributed drives.

[0006] Fourth, there is a lack of quantitative indicators for the power coordination of multiple power sources.

[0007] Therefore, those skilled in the art urgently need to improve existing methods for evaluating and analyzing the energy efficiency of distributed drive excavators. Summary of the Invention

[0008] Objective: To overcome the shortcomings of existing technologies, this invention provides a method for evaluating and analyzing the energy efficiency of a distributed drive excavator hydraulic system.

[0009] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0010] Firstly, a method for evaluating and analyzing the energy efficiency of a distributed drive excavator hydraulic system, specifically including:

[0011] Step 1: Obtain multi-source heterogeneous data of the hydraulic system of the distributed drive excavator under various working conditions.

[0012] Step 2: Construct an electro-hydraulic-mechanical multi-domain energy flow topology map based on multi-source heterogeneous data, and build the whole machine energy flow system based on the electro-hydraulic-mechanical multi-domain energy flow topology map.

[0013] Step 3: Construct a three-dimensional evaluation matrix based on the overall energy flow system, and use the three-dimensional evaluation matrix to trace the source of energy efficiency anomalies and output energy efficiency reports.

[0014] Optionally, the multi-source heterogeneous data includes: electrical domain data, hydraulic domain data, and mechanical domain data.

[0015] The electrical domain data includes: three-phase voltage, three-phase current, DC bus voltage, DC bus current, motor speed, output torque, and IGBT temperature for each drive motor.

[0016] The hydraulic domain data includes: outlet pressure of each hydraulic pump, pilot pressure, inlet and outlet pressure and flow rate of boom cylinder, stick cylinder, bucket cylinder, swing motor, and travel motor, as well as hydraulic oil temperature.

[0017] The mechanical domain data includes: displacement, angle, angular velocity of each actuator, overall machine attitude, and load on the working device.

[0018] Optionally, the electro-hydraulic-mechanical multi-domain energy flow topology includes: nodes of energy flow, and the direction of electrical energy flow between nodes of energy flow.

[0019] The nodes of the energy flow include: power supply, drive motors, hydraulic pumps, hydraulic actuators, energy recovery units, and energy storage units.

[0020] Optionally, the expression for the overall energy flow system is as follows:

[0021] [Y(s)] = [G(s)]·[U(s)]

[0022] Where [Y(s)] is the output vector, [G(s)] is the n×m dimensional transfer function matrix, and [U(s)] is the input vector.

[0023] The expression for the output vector [Y(s)] is as follows:

[0024] [Y(s)] = [P_out1, P_out2, ..., P_outn, η_sys]^T

[0025] Where P_outn represents the output power of the nth node, η_sys represents the overall system efficiency, and T is the transpose matrix.

[0026] The expression for the input vector [U(s)] is as follows:

[0027] [U(s)] = [P_in1, P_in2, ..., P_inn, f_load1, ..., f_loadn]^T

[0028] Where P_inn represents the input power of the nth node, and f_loadn represents the load capacity of the nth node.

[0029] The expression for the n×m dimensional transfer function matrix [G(s)] is as follows:

[0030] [G(s)]=

[0031] Wherein: the diagonal element G_ii(s) represents the energy transfer characteristics of the i-th actuator unit itself, and the off-diagonal element G_ij(s) (i≠j) represents the energy coupling between the i-th actuator unit and the j-th actuator unit.

[0032] Optionally, the elements of the three-dimensional evaluation matrix are calculated using the following formula:

[0033]

[0034] in, This represents the overall energy efficiency value at the time level. This represents the overall energy efficiency value of a spatial unit. This represents the overall energy efficiency value under different operating conditions. , , These are the weighting coefficients. Energy efficiency indicators over time. Energy utilization efficiency for each distributed drive unit For the distributed driving unit in the first Energy efficiency values ​​under various operating conditions.

[0035] Optional methods for tracing energy efficiency anomalies include:

[0036] Step 1: When any element in the three-dimensional evaluation matrix... The system was determined to have an energy efficiency anomaly under this "time-space-operating condition" combination; when the average energy efficiency of the entire machine... The system was determined to have abnormal overall energy efficiency. This is the energy efficiency threshold.

[0037] Step 2: Calculate the correlation coefficient between the overall energy efficiency and the energy efficiency, control parameters, and hardware operating parameters of each drive unit, and select objects with a correlation coefficient greater than the threshold as candidate objects.

[0038] Step 3: Use "Abnormal Overall Energy Efficiency" as the target node ( The candidate object is the parent node ( Hardware component failures (such as pump wear, pipeline leaks, etc.) are caused by the underlying parent node ( This forms a causal link network structure of "lower-level fault → intermediate parameters → energy efficiency anomaly".

[0039] Step 4: Calculate the anomaly probability of each parent node using multi-source heterogeneous data. And the conditional probability of the target node under the condition that the parent node is abnormal. Using Bayes' theorem, the posterior probability of each parent node causing energy efficiency anomalies is calculated.

[0040] Step 5: Take the parent node with the highest posterior probability as the root cause of the energy efficiency anomaly. If the parent node is a control parameter or hardware operating parameter, then trace it further back to the corresponding drive unit or hardware component.

[0041] Step 6: Verify the identified root cause of the anomaly, adjust the corresponding control parameters or replace the faulty hardware component, and recalculate the overall energy efficiency. If the result is positive, the tracing result is valid; otherwise, repeat steps 2-5 to re-screen candidate objects and infer the root cause.

[0042] Optionally, the method for outputting the energy efficiency report specifically includes:

[0043] Step 1: Obtain the energy efficiency indicators and comprehensive energy efficiency values ​​for each dimension in the three-dimensional evaluation matrix.

[0044] Step 2: Calculate the overall ranking score of each drive unit based on the energy efficiency indicators of each dimension.

[0045] Step 3: Calculate the energy recovery efficiency of each drive unit.

[0046] Step 4: Calculate the power coordination index of each drive unit.

[0047] Step 5: Based on the overall ranking score, energy recovery efficiency, power coordination index and anomaly tracing results, formulate targeted optimization suggestions.

[0048] Optionally, the comprehensive ranking score The expression is as follows:

[0049]

[0050] in, Energy utilization efficiency for each distributed drive unit For the first The first drive unit in the... Energy allocation weights under various operating conditions For the distributed driving unit in the first Space energy efficiency values ​​under various operating conditions This refers to the working condition dimension.

[0051] The energy recovery efficiency The expression is as follows:

[0052]

[0053] in, The total energy that the actuator can recover. This refers to energy that is actually recovered and can be reused.

[0054] The power coordination index The expression is as follows:

[0055]

[0056] in, This represents the total number of drive units; For the first The actual output power of each drive unit; : No. The optimal output power of each drive unit.

[0057] In a second aspect, a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for evaluating and analyzing the energy efficiency of a distributed drive excavator hydraulic system as described in any of the first aspects.

[0058] Thirdly, a computer device comprising:

[0059] Memory is used to store instructions.

[0060] A processor is configured to execute the instructions, causing the computer device to perform operations as described in any of the first aspects of an energy efficiency evaluation and analysis method for a distributed drive excavator hydraulic system.

[0061] Beneficial Effects: This invention provides an energy efficiency evaluation and analysis method for a distributed drive excavator hydraulic system. The method includes: simultaneously collecting multi-source data from the electrical, hydraulic, and mechanical domains; constructing an electro-hydraulic-mechanical multi-domain energy flow topology; performing independent decoupling calculations of energy efficiency for each distributed drive unit; constructing a power coordination index to quantify the power coordination of multiple power sources; evaluating energy recovery and reuse efficiency; and constructing a three-dimensional evaluation matrix and tracing the source of energy efficiency anomalies. This invention achieves refined energy efficiency evaluation of distributed drive excavators under multiple power sources, multiple energy transfer paths, and multiple operating conditions. It can identify efficiency bottlenecks, quantify the impact of power coordination on overall machine energy consumption, evaluate energy recovery efficiency, and assist in locating the causes of energy efficiency decline, providing a scientific basis for energy efficiency optimization and energy management strategy design for distributed drive excavators. Compared with existing technologies, this invention has the following beneficial effects:

[0062] 1. It has enabled independent evaluation and precise traceability of the energy efficiency of each power unit in a distributed drive excavator, filling a gap in existing technology.

[0063] 2. A multi-domain energy flow topology diagram of electro-hydraulic-mechanical systems was constructed, which can accurately identify efficiency bottlenecks in the energy transfer process.

[0064] 3. A power coordination index was proposed to quantify the impact of multi-power source collaborative control strategies on the overall energy consumption of the machine.

[0065] 4. It enables full-chain evaluation of energy recovery and reuse efficiency, providing data support for optimizing energy management strategies.

[0066] 5. Supports energy efficiency anomaly tracing based on causal inference, which can help quickly locate the cause of energy efficiency decline. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating the energy efficiency evaluation and analysis method for a distributed drive excavator hydraulic system according to the present invention.

[0068] Figure 2 This is a schematic diagram of the distributed drive excavator hydraulic system of the present invention.

[0069] Figure 3 This is a schematic diagram of the electro-hydraulic-mechanical multi-domain energy flow topology diagram of the present invention.

[0070] Figure 4 This is a schematic diagram of the energy flow topology diagram of the present invention. Detailed Implementation

[0071] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0072] The present invention will be further described below with reference to specific embodiments.

[0073] Example 1:

[0074] This embodiment introduces a method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system, such as... Figure 1 As shown, it specifically includes:

[0075] Step S1: Obtain multi-source heterogeneous data of the hydraulic system of the distributed drive excavator under various working conditions.

[0076] Among them, the distributed drive excavator hydraulic system, such as Figure 2 As shown, it includes: a battery, several motor controllers, several motors, several hydraulic pumps, several control valves, several actuators, a rotary motor, and a loading mechanism.

[0077] The batteries provide power to several motor controllers.

[0078] Each motor controller is connected to either the motor or the rotary motor.

[0079] Each motor drives a hydraulic pump via a speed reducer.

[0080] Each hydraulic pump drives the actuator via a control valve.

[0081] The rotary motor drives the upper mechanism through a reducer.

[0082] The multi-source heterogeneous data includes: electrical domain data, hydraulic domain data, and mechanical domain data.

[0083] The electrical domain data includes: three-phase voltage, three-phase current, DC bus voltage, DC bus current, motor speed, output torque, and IGBT (Insulated Gate Bipolar Transistor) temperature for each drive motor.

[0084] The hydraulic domain data includes: outlet pressure and pilot pressure of each hydraulic pump, inlet and outlet pressure, flow rate, and hydraulic oil temperature of each actuator (boom cylinder, stick cylinder, bucket cylinder, swing motor, travel motor).

[0085] The mechanical domain data includes: displacement, angle, angular velocity of each actuator, overall machine attitude (measured by IMU), and load on the working device.

[0086] Step S2: Construct an electro-hydraulic-mechanical multi-domain energy flow topology map based on multi-source heterogeneous data, and build the whole machine energy flow system based on the electro-hydraulic-mechanical multi-domain energy flow topology map.

[0087] The electro-hydraulic-mechanical multi-domain energy flow topology diagram, such as... Figure 3 As shown, this includes: the nodes of energy flow, and the direction of electrical energy flow between the nodes of energy flow.

[0088] The nodes of the energy flow include: power supply, drive motors, hydraulic pumps, hydraulic actuators, energy recovery units, and energy storage units.

[0089] The overall energy flow system is a multiple-input multiple-output system, and its expression is as follows:

[0090] [Y(s)] = [G(s)]·[U(s)]

[0091] Where [Y(s)] is the output vector, [G(s)] is the n×m dimensional transfer function matrix, and [U(s)] is the input vector.

[0092] The expression for the output vector [Y(s)] is as follows:

[0093] [Y(s)] = [P_out1, P_out2, ..., P_outn, η_sys]^T

[0094] Where P_outn represents the output power of the nth node, η_sys represents the overall system efficiency, and T is the transpose matrix.

[0095] Wherein: the total system efficiency η_sys is the sum of the efficiencies of each node on the energy flow path, including: motor efficiency, inverter efficiency, hydraulic pump efficiency, pipeline transmission efficiency, and hydraulic cylinder / motor efficiency.

[0096] A working condition identification module is introduced to identify the current working condition (digging, rotation, unloading, walking, and combined actions) through pilot pressure signals and attitude sensor data, and to calculate the independent energy efficiency of each unit under each working condition. .

[0097] Among them, the efficiency of each node The energy level of each distributed drive unit is represented by the following expression:

[0098]

[0099] Where i represents the i-th distributed driving unit. Indicates the energy efficiency of the distributed drive unit. This represents the effective output energy of the distributed drive unit. This represents the input energy of the distributed drive unit.

[0100] η_sys = (E1 + E2 + ... + E n ) / E z

[0101] Among them, E n E represents the output energy of the nth actuator during a certain time period t. z This indicates the electrical energy output of the power battery within the time period t.

[0102] The expression for the input vector [U(s)] is as follows:

[0103] [U(s)] = [P_in1, P_in2, ..., P_inn, f_load1, ..., f_loadn]^T

[0104] Where P_inn represents the input power of the nth node, and f_loadn represents the load capacity of the nth node.

[0105] The n×m dimensional transfer function matrix [G(s)] is used to describe the coupling relationship of each energy path.

[0106] This also includes: analysis of the energy transfer characteristics and energy coupling effects of the whole machine energy flow system.

[0107] For a distributed system with n independent actuator units, its own energy transfer characteristics and the energy coupling between actuator units can be analyzed using the following transfer function:

[0108] [G(s)]=

[0109] Wherein: the diagonal element G_ii(s) represents the energy transfer characteristics of the i-th actuator unit itself, and the off-diagonal element G_ij(s) (i≠j) represents the energy coupling between actuator units.

[0110] This also includes: constructing an energy flow topology diagram based on the overall machine's energy flow system, such as... Figure 4 As shown, this diagram illustrates the energy flow characteristics throughout the entire transmission process, from the output of electrical energy from the power battery, through energy conversion in the hydraulic system, to its final application to various actuators.

[0111] Step S3: Construct a three-dimensional evaluation matrix based on the overall energy flow system, and use the three-dimensional evaluation matrix to trace the source of energy efficiency anomalies and output an energy efficiency report.

[0112] The element expression of the three-dimensional evaluation matrix is ​​as follows:

[0113] ,

[0114] in, This represents the overall energy efficiency value at the time level. This represents the overall energy efficiency value of a spatial unit. This represents the overall energy efficiency value under the operating condition dimension, with a value range of [0,1]. The larger the value, the better the energy efficiency.

[0115] in, It is a time-level hierarchy, including three levels: transient (…). ),cycle( ),cycle( The corresponding time scales are, in order, ms level, action level (such as a single excavation), and job level (such as a 1-hour job).

[0116] It is a spatial unit, containing distributed drive units (such as boom drive units). , stick drive unit Bucket drive unit Rotary drive unit Walking drive unit ) and each actuator (corresponding one-to-one with the drive unit).

[0117] From the perspective of working conditions, it includes four typical working conditions, namely excavation working conditions ( ), slewing condition ( ), walking conditions ( ), composite motion conditions ( ).

[0118] in, The calculation formula is as follows:

[0119]

[0120] In the formula: , , Let be the weighting coefficient, satisfying It can be adjusted according to actual operational needs (default value: , , (Highlighting the distributed characteristics of the spatial dimension).

[0121] As a time-dimensional energy efficiency indicator, it calculates time-dimensional energy efficiency based on energy input and effective output at different time scales, specifically including:

[0122] Transient energy efficiency ( (millisecond level, reflecting instantaneous energy utilization efficiency)

[0123]

[0124] In the formula: The transient hydraulic system input power (unit: kW) refers to the hydraulic pump output power of the distributed drive unit. Transient effective output power (unit: kW), which is the power required for the actuator to overcome the load.

[0125] Cyclic Energy Efficiency ( (Action-level, such as a single mining loop):

[0126]

[0127] In the formula: The time for a single action cycle (in seconds); For effective energy input within the cycle; The effective output energy within the cycle (unit: kJ).

[0128] Cycle Energy Efficiency ( (Job-level, such as a 1-hour work cycle):

[0129]

[0130] In the formula: The number of action cycles contained within the period; , The first Effective input energy and effective output energy (unit: kJ) for each action cycle.

[0131] Spatial dimension energy efficiency index To calculate the energy utilization efficiency for each distributed drive unit (and its corresponding actuator), the expression is as follows:

[0132]

[0133] In the formula:

[0134] : No. The total input energy (unit: kJ) of each distributed drive unit is equal to the input energy of the hydraulic pump.

[0135] : No. The output energy of the actuator corresponding to each distributed drive unit (unit: kJ);

[0136] : No. The total energy loss (unit: kJ) of each distributed drive unit includes hydraulic pump losses, pipeline leakage losses, and motor losses. .

[0137] Energy efficiency indicators under operating conditions For the distributed driving unit in the first The expression for the energy efficiency value under this operating condition is as follows:

[0138]

[0139] In the formula: Total number of distributed driver units (in this method) );

[0140] : No. The first drive unit in the... Energy allocation weights under various operating conditions satisfy The proportion of input energy of each drive unit under operating conditions is determined by ( ).

[0141] The methods for tracing the source of energy efficiency anomalies specifically include:

[0142] This method combines correlation analysis and causal inference to identify abnormal overall energy efficiency (i.e., comprehensive energy efficiency value). , (The energy efficiency threshold is set to 0.6 by default.) The process traces back to specific drive units, control parameters, or hardware components. The steps are clear and quantifiable, as detailed below:

[0143] 3.1. Criteria for Determining Abnormalities:

[0144] First, set the energy efficiency threshold. When any element in the three-dimensional evaluation matrix The system was determined to have an energy efficiency anomaly under this "time-space-operating condition" combination; when the average energy efficiency of the entire machine... The system was determined to have abnormal overall energy efficiency.

[0145] 3.2 Step 1: Correlation Analysis (Locating Abnormal Related Objects):

[0146] The correlation between the overall system energy efficiency and the energy efficiency, control parameters, and hardware operating parameters of each drive unit is analyzed using the Pearson correlation coefficient. Objects with a high correlation to anomalies are then identified. The specific formula is as follows:

[0147]

[0148] In the formula:

[0149] Pearson correlation coefficient, with a value range of [-1, 1]. Indicates a strong correlation. Indicates moderate relevance. Indicates a weak correlation;

[0150] Overall Energy Efficiency Series ( (Time-varying sequence)

[0151] The sequence of objects to be analyzed (which can be the energy efficiency sequence of each drive unit, the control parameter sequence, or the hardware operating parameter sequence).

[0152] : Sample size (i.e., number of monitoring data points); , Sequences , The average value.

[0153] Description of the analysis objects and corresponding sequences:

[0154] Drive unit sequence: each A sequence of changes over time;

[0155] Control parameter sequence: pump displacement Motor speed Valve group opening The sequence of changes over time;

[0156] Hardware operating parameter sequence: Hydraulic pump outlet pressure Pipeline temperature Leakage amount The sequence of changes over time.

[0157] Filtering rules: Keep The object is used as a candidate object for anomaly tracing.

[0158] 3.3 Second Step: Causal Inference (Identifying the Root Cause of the Anomaly):

[0159] Based on Bayesian networks, causal inference is performed on the selected candidates to determine the root cause of energy efficiency anomalies (specific driving unit, control parameters, or hardware components). The steps are as follows:

[0160] Constructing a Bayesian network structure: with "abnormal overall energy efficiency" as the target node ( The candidate object is the parent node ( Hardware component failures (such as pump wear or pipeline leaks) are caused by the underlying parent node ( This forms a causal chain of "lower-level fault → intermediate parameters → energy efficiency anomaly".

[0161] Determine the prior probability of nodes: Calculate the anomaly probability of each parent node (candidate object) using historical monitoring data. And the conditional probability of the target node under the condition that the parent node is abnormal. ,For example: (When the pump is worn, the probability of abnormal energy efficiency is 85%).

[0162] Calculate the posterior probability: Using Bayes' theorem, calculate the posterior probability that each parent node causes energy efficiency anomalies, as shown in the following formula:

[0163]

[0164] Posterior probability The larger the value, the greater the likelihood that the parent node is the root cause of the energy efficiency anomaly.

[0165] Root cause determination: The parent node with the highest posterior probability is taken as the root cause of the energy efficiency anomaly. If the parent node is a control parameter or hardware operating parameter, the cause is further traced back to the corresponding drive unit or hardware component (e.g., if there is a pipeline leak). If the root cause is found, it can be traced back to the hydraulic piping components of the corresponding drive unit.

[0166] 3.4 Anomaly tracing and verification:

[0167] Verify the identified root cause of the anomaly, adjust the corresponding control parameters (such as correcting the pump displacement) or replace the faulty hardware component, and recalculate the overall energy efficiency. If the result is positive, the tracing result is valid; otherwise, repeat steps 3.2-3.3 to re-screen candidate objects and infer the root cause.

[0168] The energy efficiency report output specifically includes:

[0169] The energy efficiency report, based on a three-dimensional assessment matrix and anomaly tracing results, comprehensively outputs the system's energy efficiency status, root causes of anomalies, and optimization suggestions. The steps are clear and the indicators are quantified, as detailed below:

[0170] 3.1 Report Output Steps:

[0171] Step 1: Data Preprocessing (Basic Preparation):

[0172] Collect monitoring data of the hydraulic system of the distributed drive excavator, including: input / output energy of each drive unit, control parameters (pump displacement, motor speed, etc.), hardware operating parameters (pressure, temperature, leakage, etc.), working condition data, and time dimension data. Remove abnormal data (such as sensor failure data), standardize the data, and substitute it into the three-dimensional evaluation matrix formula to calculate the energy efficiency index of each dimension and the comprehensive energy efficiency value.

[0173] Step 2: Energy efficiency ranking of each distributed drive unit:

[0174] Energy efficiency in spatial dimensions Using the core indicator and considering operational adaptability, the comprehensive ranking score of each drive unit is calculated using the following formula:

[0175]

[0176] In the formula: For the first The ranking score of each drive unit; For the first The first drive unit in the... Energy allocation weights under various operating conditions; For the first Energy efficiency values ​​for various operating conditions.

[0177] Ranking rules: by The energy efficiency scores are sorted from highest to lowest, with higher scores ranking higher. The energy efficiency shortcomings of each drive unit under different operating conditions are also marked.

[0178] Step 3: Calculation of energy recovery efficiency:

[0179] The energy recovery of the hydraulic system of a distributed drive excavator mainly comes from the potential / kinetic energy recovery during the braking and descent processes of the actuators. The energy recovery efficiency is calculated using the following formula:

[0180]

[0181] In the formula:

[0182] Total recoverable energy of the actuator (unit: kJ), which is the sum of potential and kinetic energy released during braking / descent.

[0183] The actual energy recovered and reusable (unit: kJ) is the energy absorbed by the energy storage unit (such as an energy storage device) minus the losses during the recovery process (pipeline losses, energy storage unit losses).

[0184] At the same time, the energy recovery efficiency of each drive unit is output (calculated according to the above formula), and the differences in recovery effect are compared and analyzed.

[0185] Step 4: Power Coordination Index:

[0186] Power coordination reflects the rationality of power allocation among distributed drive units, avoiding energy loss caused by power redundancy or insufficiency. The calculation formula is as follows:

[0187]

[0188] In the formula:

[0189] Power coordination index, with a value range of [0,1]. The larger the value, the better the coordination.

[0190] Total number of drive units;

[0191] : No. The actual output power of each drive unit (unit: kW);

[0192] : No. The optimal output power (in kW) of each drive unit is determined by the operating conditions and the goal of maximizing system energy efficiency. , (Actuator load power).

[0193] Scoring criteria: 9 is excellent. To be qualified, This is unacceptable (power allocation needs to be optimized).

[0194] Step 5: Integration of anomaly tracing results:

[0195] Organize the root causes of anomalies (specific driving units, control parameters, or hardware components) obtained by "correlation analysis + causal inference", and clarify the abnormal manifestations (such as the energy efficiency of a certain driving unit being lower than the threshold, the deviation of a certain control parameter being too large, or the failure of a certain hardware component) and the scope of the abnormal impact (the time level and operating conditions involved).

[0196] Step 6: Develop optimization suggestions (based on quantitative indicators)

[0197] By combining energy efficiency rankings, energy recovery efficiency, power coordination scores, and anomaly tracing results, targeted optimization suggestions are formulated to ensure that they are feasible and verifiable.

[0198] Step 7: Report Integration and Output:

[0199] Integrate all the above content according to the standard format to form a complete energy efficiency report, ensuring that the data is accurate, the formulas are clear, and the recommendations are specific.

[0200] 3.2 Core Contents of the Energy Efficiency Report:

[0201] 1) Example of energy efficiency ranking for each distributed drive unit:

[0202] Space efficiency of 5 drive units The weights are as follows: boom (0.72), stick (0.68), bucket (0.65), swing (0.75), and travel (0.60); the weights of each drive unit under combined working conditions. The corresponding values ​​are 0.25, 0.23, 0.20, 0.18, and 0.14; combined operating condition energy efficiency. The ranking score is:

[0203] Rotary drive unit: (Rank 1)

[0204] Boom drive unit: (Ranked 2)

[0205] Stick drive unit: (Ranked 3)

[0206] Bucket drive unit: (Rank 4)

[0207] Walking drive unit: (Ranked 5th)

[0208] 2) Example of energy recovery efficiency:

[0209] Potential energy can be recovered during boom descent. The energy storage unit actually recovers energy The energy recovery efficiency of the boom drive unit is:

[0210]

[0211] 3) Example of power coordination index

[0212] Actual output power of 5 drive units The corresponding output powers are 15kW, 13kW, 11kW, 10kW, and 8kW; optimal output power. If the power ratings are 14kW, 12kW, 12kW, 10kW, and 9kW respectively, then the power coordination index is:

[0213]

[0214] An index of ≥0.9 is considered excellent, indicating that the power distribution among the drive units is coordinated and reasonable.

[0215] 4) Example of optimization suggestions:

[0216] For the walking drive unit (ranked last in energy efficiency): optimize its hydraulic pump displacement control parameters and reduce pipeline leakage losses, with the goal of... Increased to above 0.65;

[0217] Regarding energy recovery efficiency: Optimize the energy charging and discharging control strategy of the controller to reduce energy loss during the recovery process, with the goal of increasing the overall energy recovery efficiency to over 85%.

[0218] For the root cause of the abnormality (such as leakage in the slewing drive unit pipeline): replace the leaking pipeline, recalibrate the pipeline pressure, and verify whether the energy efficiency has recovered to above the threshold.

[0219] Example 2:

[0220] This embodiment describes a computer-readable storage medium storing a computer program that, when executed by a processor, implements an energy efficiency evaluation and analysis method for a distributed drive excavator hydraulic system as described in any of Embodiments 1.

[0221] Example 3:

[0222] This embodiment describes a computer device, including:

[0223] Memory is used to store instructions.

[0224] A processor is configured to execute the instructions, causing the computer device to perform the operation of an energy efficiency evaluation and analysis method for a distributed drive excavator hydraulic system as described in any of Embodiment 1.

[0225] Example 4:

[0226] The first embodiment of this example takes the evaluation of the slewing energy recovery efficiency of a distributed drive excavator as an example. It uses a distributed hydraulic excavator with independent electric slewing drive as the object to test its slewing energy recovery efficiency.

[0227] The excavator's power system architecture is as follows: the power battery is connected to the swing motor controller, left travel motor controller, right travel motor controller, and main pump motor controller via a DC bus. The swing motor has a braking energy recovery function, and the recovered energy can be fed back to the DC bus for use by other motors or stored in the energy storage unit.

[0228] Test conditions: Continuous 90° rotation loading operation, i.e., the typical cycle of digging-rotating-unloading-returning. The test lasted 30 minutes and approximately 120 operation cycles were completed.

[0229] Execute step S1 to simultaneously collect electrical domain data (DC bus voltage, rotary motor current, speed), hydraulic domain data (rotary motor inlet and outlet pressure, flow rate), and mechanical domain data (rotation angle, angular velocity).

[0230] Execute step S2 to construct an energy flow topology diagram and identify the following energy conversion links in the rotary drive subsystem: electrical energy → (inverter) → motor shaft mechanical energy → (hydraulic pump) → hydraulic energy → (rotary motor) → rotary mechanical energy → (braking) → regenerated electrical energy.

[0231] Step S3 is executed to perform energy efficiency decoupling calculations for each rotation cycle. Data from one typical rotation cycle is analyzed: 45.2 kJ of electrical energy is consumed during the acceleration phase, 12.8 kJ during the constant speed phase, and 28.6 kJ of electrical energy is recovered during the deceleration and braking phase. The net energy consumption for this cycle is 45.2 + 12.8 - 28.6 = 29.4 kJ. The effective hydraulic energy output during rotation is 38.5 kJ (calculated based on the integral of pressure and flow). Therefore, the energy efficiency of the rotation drive unit is 38.5 / 45.2 = 85.2%, and the recovery efficiency is 28.6 / (rotation kinetic energy + partial hydraulic energy) = 67.3%.

[0232] The average recovery efficiency after 120 cycles was 64.8%. Further analysis revealed that the average recovery efficiency was 68.2% in the morning (when the hydraulic oil temperature was lower), but dropped to 61.5% in the afternoon (when the hydraulic oil temperature rose above 85°C). Based on the temperature data, it was determined that high temperatures increased leakage in the hydraulic motor and pipelines, affecting the recovery efficiency. System output optimization recommendations: increase the power of the hydraulic oil cooler or optimize the recovery strategy threshold under high-temperature conditions.

[0233] This embodiment verifies the effectiveness of the method of the present invention in evaluating the efficiency of rotary energy recovery and tracing the source of anomalies.

[0234] The second embodiment of this example uses the power coordination diagnosis of compound actions as an example to conduct a comparative energy efficiency evaluation of two identical distributed drive excavators (prototype M and prototype N). The two prototypes performed compound actions (slewing + boom lifting + stick retraction) under the same working conditions, with each operation lasting 1 hour.

[0235] By performing steps S1 to S3, the following key indicators are calculated:

[0236] Table 1 shows the key performance indicators for prototype M and prototype N.

[0237]

[0238] It is evident that the overall energy efficiency of prototype N is significantly lower than that of prototype M, but the energy efficiency differences among individual drive units are not significant (all within 1 percentage point). The problem mainly lies in power coordination. The power coordination index of prototype N is only 0.84, which is lower than the preset threshold of 0.90.

[0239] Further analysis of the instantaneous power curves revealed that during the combined operation of prototype N, the power peaks of the rotary motor and the main pump motor frequently overlapped, causing the instantaneous total power demand to exceed the battery's maximum discharge capacity (150 kW). This resulted in the bus voltage dropping from 580V to 510V, triggering the inverter's power limiting protection, and forcing each motor to operate at a reduced rate, leading to additional energy loss. In contrast, prototype M's control strategy actively staggered the peak power demand times of each motor, smoothing out the total power demand and stabilizing the bus voltage above 550V.

[0240] The source analysis determined that the controller parameters (compound action power distribution coefficient) of prototype N were improperly set. System output optimization recommendations: Adjust the power distribution strategy during compound actions, staggering the acceleration times of the rotary motor and the main pump motor by 200ms, or reduce the peak power limit value during compound actions.

[0241] This embodiment verifies the application value of the method of the present invention in power coordination evaluation and control strategy optimization.

[0242] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating and analyzing the energy efficiency of a distributed drive excavator hydraulic system, characterized in that: Specifically, it includes: Step 1: Obtain multi-source heterogeneous data of the hydraulic system of the distributed drive excavator under various working conditions; Step 2: Construct an electro-hydraulic-mechanical multi-domain energy flow topology map based on multi-source heterogeneous data, and build the whole machine energy flow system based on the electro-hydraulic-mechanical multi-domain energy flow topology map; Step 3: Construct a three-dimensional evaluation matrix based on the overall energy flow system, and use the three-dimensional evaluation matrix to trace the source of energy efficiency anomalies and output energy efficiency reports.

2. The method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system according to claim 1, characterized in that: The multi-source heterogeneous data includes: electrical domain data, hydraulic domain data, and mechanical domain data; The electrical domain data includes: three-phase voltage, three-phase current, DC bus voltage, DC bus current, motor speed, output torque, and IGBT temperature of each drive motor. The hydraulic domain data includes: outlet pressure of each hydraulic pump, pilot pressure, boom cylinder, stick cylinder, bucket cylinder, swing motor, and travel motor, inlet and outlet pressure, flow rate, and hydraulic oil temperature. The mechanical domain data includes: displacement, angle, angular velocity of each actuator, overall machine attitude, and load on the working device.

3. The method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system according to claim 1, characterized in that: The electro-hydraulic-mechanical multi-domain energy flow topology includes: nodes of energy flow, and the direction of electrical energy flow between nodes of energy flow; The nodes of the energy flow include: power supply, drive motors, hydraulic pumps, hydraulic actuators, energy recovery units, and energy storage units.

4. The method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system according to claim 1, characterized in that: The expression for the overall energy flow system is as follows: [Y(s)] = [G(s)]·[U(s)]; Where [Y(s)] is the output vector, [G(s)] is the n×m dimensional transfer function matrix, and [U(s)] is the input vector; The expression for the output vector [Y(s)] is as follows: [Y(s)] = [P_out1, P_out2, ..., P_outn, η_sys]^T; Where P_outn represents the output power of the nth node, η_sys represents the overall system efficiency, and T is the transpose matrix; The expression for the input vector [U(s)] is as follows: [U(s)] = [P_in1, P_in2, ..., P_inn, f_load1, ..., f_loadn]^T; Where P_inn represents the input power of the nth node, and f_loadn represents the load capacity of the nth node; The expression for the n×m dimensional transfer function matrix [G(s)] is as follows: [G(s)]= ; Wherein: the diagonal element G_ii(s) represents the energy transfer characteristics of the i-th actuator unit itself, and the off-diagonal element G_ij(s) (i≠j) represents the energy coupling between the i-th actuator unit and the j-th actuator unit.

5. The method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system according to claim 1, characterized in that: The formula for calculating the elements of the three-dimensional evaluation matrix is ​​as follows: ； in, This represents the overall energy efficiency value at the time level. This represents the overall energy efficiency value of a spatial unit. This represents the overall energy efficiency value under different operating conditions. , , These are the weighting coefficients. Energy efficiency indicators over time. Energy utilization efficiency for each distributed drive unit For the distributed driving unit in the first Energy efficiency values ​​under various operating conditions.

6. The method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system according to claim 1, characterized in that: Methods for tracing the source of energy efficiency anomalies specifically include: Step 1: When any element in the three-dimensional evaluation matrix... The system was determined to have an energy efficiency anomaly under this "time-space-operating condition" combination; when the average energy efficiency of the entire machine... The system was determined to have abnormal overall energy efficiency. Energy efficiency threshold; Step 2: Calculate the correlation coefficient between the overall energy efficiency and the energy efficiency, control parameters, and hardware operating parameters of each drive unit, and filter out objects with correlation coefficients greater than the threshold as candidate objects; Step 3: Set "Abnormal Overall Energy Efficiency" as the target node ( The candidate object is the parent node ( Hardware component failures (such as pump wear, pipeline leaks, etc.) are caused by the underlying parent node ( This forms a causal link network structure of "lower-level fault → intermediate parameters → energy efficiency anomaly"; Step 4: Calculate the anomaly probability of each parent node using multi-source heterogeneous data. And the conditional probability of the target node under the condition that the parent node is abnormal. Using Bayes' theorem, the posterior probability of each parent node causing energy efficiency anomalies is calculated. Step 5: Take the parent node with the highest posterior probability as the root cause of the energy efficiency anomaly. If the parent node is a control parameter or hardware operating parameter, then trace it further back to the corresponding drive unit or hardware component. Step 6: Verify the identified root cause of the anomaly, adjust the corresponding control parameters or replace the faulty hardware component, and recalculate the overall energy efficiency. If the result is positive, the tracing result is valid; otherwise, repeat steps 2-5 to re-screen candidate objects and infer the root cause.

7. The method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system according to claim 1, characterized in that: The method for outputting the energy efficiency report specifically includes: Step 1: Obtain the energy efficiency indicators and overall energy efficiency values ​​for each dimension of the three-dimensional evaluation matrix; Step 2: Calculate the overall ranking score of each drive unit based on the energy efficiency indicators of each dimension; Step 3: Calculate the energy recovery efficiency of each drive unit; Step 4: Calculate the power coordination index of each drive unit; Step 5: Based on the overall ranking score, energy recovery efficiency, power coordination index and anomaly tracing results, formulate targeted optimization suggestions.

8. The method for energy efficiency evaluation and analysis of a distributed drive excavator hydraulic system according to claim 7, characterized in that: The overall ranking score The expression is as follows: ； in, Energy utilization efficiency for each distributed drive unit For the first The first drive unit in the... Energy allocation weights under various operating conditions For the distributed driving unit in the first Space energy efficiency values ​​under various operating conditions From the perspective of working conditions; The energy recovery efficiency The expression is as follows: ； in, The total energy that the actuator can recover. Energy that is actually recovered and can be reused; The power coordination index The expression is as follows: ； in, This represents the total number of drive units; For the first The actual output power of each drive unit; : No. The optimal output power of each drive unit.

9. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements a method for evaluating and analyzing the energy efficiency of a distributed drive excavator hydraulic system as described in any one of claims 1 to 8.

10. A computer device, characterized in that: include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the computer device to perform the operation of the energy efficiency evaluation and analysis method for a distributed drive excavator hydraulic system as described in any one of claims 1 to 8.

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