A motor dynamic efficiency optimization control system

Abstract

The application discloses a motor dynamic efficiency optimization regulation system, and relates to the technical field of dynamic efficiency optimization regulation, which comprises a data acquisition module, an efficiency characteristic module, a health assessment module, a state assessment module, an optimization execution module and a parameter updating module. Through double-dimension assessment of the efficiency characteristic and the system health degree, a comprehensive index reflecting the motor operation state is constructed, the change from single efficiency optimization to system-level comprehensive optimization is realized, and the parameter updating module forms a complete closed-loop control, so that the system can dynamically adjust key parameters according to actual operation effects, has self-learning and self-optimization functions, and can dynamically adjust key parameters according to actual operation effects. A multi-mode operation strategy realizes the best balance between safety and economy, ensures equipment safety, and dynamically optimizes construction efficiency under the premise of safety.

Description

Technical Field

[0001] This invention relates to the field of dynamic efficiency optimization and control technology, and in particular to a dynamic efficiency optimization and control system for electric motors. Background Technology

[0002] As a core power source in industrial production and daily life, the operating efficiency of electric motors directly affects energy utilization efficiency and system operating costs. In applications such as electric vehicles, intelligent equipment, and industrial automation, electric motors often need to operate under complex and ever-changing dynamic conditions, which places higher demands on motor efficiency optimization.

[0003] Currently, mainstream motor efficiency optimization methods have significant shortcomings in dynamic efficiency optimization. Traditional control methods based on static efficiency MAPs, although simple to implement, rely heavily on preset steady-state operating data and cannot adapt to dynamic changes during motor operation. When the motor faces actual situations such as sudden load changes, speed fluctuations, or changes in ambient temperature, the static optimization parameters often deviate from the actual optimal operating point, resulting in a significant decrease in efficiency during the dynamic process.

[0004] Another optimization method based on online search, such as the perturbation and observation method, can adapt to changes in operating conditions to some extent. However, due to its relatively simple data processing mechanism, which makes decisions based only on local power changes, it suffers from slow optimization speed and response lag. In scenarios where motor operating conditions change rapidly, this method is difficult to achieve real-time dynamic efficiency optimization and may even lead to a decrease in system stability due to continuous search perturbations.

[0005] As the complexity of modern motor systems continues to increase, traditional simple data processing methods are no longer sufficient to meet the real-time requirements of dynamic efficiency optimization. The large amount of state data generated by the motor during operation, including multi-dimensional information such as voltage, current, speed, and temperature, requires advanced data processing algorithms for real-time analysis and intelligent decision-making. Existing technologies often use simple threshold judgment or linear filtering methods to process this data, lacking in-depth analysis and dynamic optimization capabilities of the system's operating state, resulting in limited efficiency optimization effects under complex operating conditions. Summary of the Invention

[0006] In response to the above situation, the present invention can construct a comprehensive index that fully reflects the motor's operating status through a two-dimensional evaluation of efficiency characteristics and system health, thereby achieving dynamic efficiency optimization of the motor across the entire operating range.

[0007] The technical solution includes a data acquisition module, an efficiency characteristic module, a health assessment module, a status assessment module, an optimization execution module, and a parameter update module. The data acquisition module collects the three-phase current I, speed ω, output torque T_e, winding temperature T_s, and DC bus voltage U_dc of the motor in real time.

[0008] The efficiency feature module is connected to the data acquisition module and calculates the comprehensive efficiency feature value CEF based on the acquired I, ω, T_e, T_s and U_dc.

[0009] The health assessment module is connected to the data acquisition module and calculates the system health factor H based on the acquired I, ω, T_e, T_s and U_dc.

[0010] The status assessment module is connected to the efficiency feature module and the health assessment module. Based on the comprehensive efficiency feature value CEF and the system health factor H, it calculates the overall operating status index S and classifies the operating status level according to the S value.

[0011] The optimization execution module is connected to the status evaluation module, generates optimization control instructions based on the running status level, executes the control instructions and collects feedback data;

[0012] The parameter update module is connected to the optimization execution module and is used to receive and process the feedback data, and dynamically update the key parameters in the efficiency feature module and the health assessment module.

[0013] Furthermore, the efficiency feature module calculates the comprehensive efficiency feature value (CEF) in the following way:

[0014] CEF=(P_out / P_in)×(1-|T_e-T_opt| / T_range)×f_T(T_s);

[0015] Where P_out=ω×T_e represents the output power; P_in=U_dc×I_avg represents the input power, I_avg is the effective value of the three-phase current I; T_opt is the optimal torque at the current speed ω determined based on the motor efficiency MAP diagram and / or online learning; T_range is the allowable torque range determined based on the motor performance; and f_T(T_s) is the temperature effect function.

[0016] Furthermore, the temperature influence function f_T(T_s) is calculated as follows:

[0017] When T_s≤T_ref, f_T(T_s)=1;

[0018] When T_s>T_ref, f_T(T_s)=1-k×(T_s-T_ref) / (T_max-T_ref);

[0019] Where T_ref is the reference temperature, T_max is the maximum allowable temperature, and k is the temperature influence coefficient.

[0020] Furthermore, the health assessment module calculates the system's health factor H in the following way:

[0021] H = 1 / (1 + D_total);

[0022] Where D_total=α×|I / I_nom|+β×|T_s / T_max|+γ×|(dω / dt) / ω_max|, I_nom is the rated current of the motor, T_max is the maximum allowable temperature corresponding to the insulation class of the motor, ω_max is the maximum allowable speed of the motor, dω / dt is the speed change rate, α, β, and γ are weighting coefficients, and α+β+γ=1.

[0023] Furthermore, the weighting coefficients α, β, and γ are calculated as follows:

[0024] When D_total≠0, α=|I / I_nom| / D_total,β=|T_s / T_max| / D_total,γ=|(dω / dt) / ω_max| / D_total;

[0025] When D_total=0, α=β=γ=1 / 3.

[0026] Furthermore, the state assessment module calculates the overall operating state index S in the following way:

[0027] S = CEF × H;

[0028] The operating status levels are divided into:

[0029] Excellent state: S≥0.8;

[0030] Good condition: 0.6 ≤ S < 0.8;

[0031] General condition: 0.4 ≤ S < 0.6;

[0032] Differential state: S<0.4.

[0033] Furthermore, the optimized execution module includes:

[0034] The decision-making unit selects optimization strategies based on the operational status level and generates control commands;

[0035] The execution unit converts control commands into drive signals to control the operation of the motor.

[0036] The feedback data acquisition unit acquires new system status data after the control command is executed, uses it as feedback data, and sends it to the parameter update module. The feedback data includes the three-phase current, speed, output torque, winding temperature, and DC bus voltage after execution.

[0037] Furthermore, the decision-making unit selects an optimization strategy based on the operational status level:

[0038] Superior state: Maintain current control parameters and make fine adjustments to maintain high efficiency;

[0039] Good condition: Adjust the right-angle axis current distribution to improve efficiency;

[0040] Normal state: Adjust control parameters and strengthen monitoring and early warning;

[0041] Poor condition: Implement reduced operating capacity or shutdown protection measures.

[0042] Furthermore, the parameter update module includes:

[0043] The model parameter correction unit receives feedback data from the optimization execution module, and updates the optimal torque parameter T_opt in the efficiency feature module iteratively using the least squares method based on the rotational speed ω and output torque T_e in the feedback data; and adaptively adjusts the temperature influence coefficient k in the temperature influence function based on the winding temperature T_s in the feedback data.

[0044] The evaluation benchmark update unit receives feedback data from the optimization execution module, records and analyzes the extreme values ​​of current, temperature and speed in the historical operating data, and updates the numerical benchmarks of rated current I_nom, maximum allowable temperature T_max and maximum allowable speed ω_max used in the health evaluation module for calculation accordingly.

[0045] Furthermore, the data acquisition module synchronously acquires all parameters at a sampling frequency of not less than 10kHz, and includes a timestamp identifier.

[0046] Due to the adoption of the above technical solutions, the present invention has the following advantages compared with the prior art;

[0047] By evaluating efficiency characteristics and system health from two dimensions, a comprehensive index reflecting the motor's operating status was constructed, realizing the transformation from single efficiency optimization to system-level comprehensive optimization. The parameter update module forms a complete closed-loop control, enabling the system to dynamically adjust key parameters based on actual operating results. It has self-learning and self-optimization functions. The multi-mode operation strategy achieves the best balance between safety and economy, ensuring equipment safety while dynamically optimizing construction efficiency under the premise of safety. Attached Figure Description

[0048] Figure 1 This is a flowchart of a dynamic efficiency optimization and control system for electric motors according to the present invention.

[0049] Figure 2 This is a flowchart of the optimization execution module of a dynamic efficiency optimization and control system for electric motors according to the present invention.

[0050] Figure 3 This is a flowchart of the parameter update module of a dynamic efficiency optimization and control system for electric motors according to the present invention. Detailed Implementation

[0051] The foregoing and other technical contents, features and effects of the present invention are described in conjunction with the appendix below. Figures 1 to 3 The detailed description of the embodiments will make this clear. All structural details mentioned in the following embodiments are based on the accompanying drawings.

[0052] Example 1, based on existing technology, includes a data acquisition module, an efficiency characteristic module, a health assessment module, a status assessment module, an optimization execution module, and a parameter update module. The data acquisition module collects the three-phase current I, speed ω, output torque T_e, winding temperature T_s, and DC bus voltage U_dc of the motor in real time.

[0053] The efficiency feature module is connected to the data acquisition module and calculates the comprehensive efficiency feature value CEF based on the acquired I, ω, T_e, T_s and U_dc.

[0054] The health assessment module is connected to the data acquisition module and calculates the system health factor H based on the acquired I, ω, T_e, T_s and U_dc.

[0055] The status assessment module is connected to the efficiency feature module and the health assessment module. Based on the comprehensive efficiency feature value CEF and the system health factor H, it calculates the overall operating status index S and classifies the operating status level according to the S value.

[0056] The optimization execution module is connected to the status evaluation module, generates optimization control instructions based on the running status level, executes the control instructions and collects feedback data;

[0057] The parameter update module is connected to the optimization execution module and is used to receive and process the feedback data, and dynamically update the key parameters in the efficiency feature module and the health assessment module.

[0058] Furthermore, the efficiency feature module calculates the comprehensive efficiency feature value (CEF) in the following way:

[0059] CEF=(P_out / P_in)×(1-|T_e-T_opt| / T_range)×f_T(T_s);

[0060] Where P_out=ω×T_e represents the output power; P_in=U_dc×I_avg represents the input power, I_avg is the effective value of the three-phase current I; T_opt is the optimal torque at the current speed ω determined based on the motor efficiency MAP diagram and / or online learning; T_range is the allowable torque range determined based on the motor performance; and f_T(T_s) is the temperature effect function.

[0061] The specific calculation steps for the comprehensive efficiency characteristic value CEF are as follows:

[0062] Data preprocessing: The collected raw data is filtered by moving average with a window size of 10 sampling points;

[0063] Calculate the effective value of the three-phase current: I_avg=sqrt((I_a²+I_b²+I_c²) / 3);

[0064] Calculate the input power: P_in = U_dc × I_avg, taking into account the conduction loss compensation of power devices;

[0065] Calculate the output power: P_out = ω × T_e, based on real-time speed and torque measurements;

[0066] Query T_opt: Select the appropriate T_opt determination method based on the current running mode;

[0067] Temperature compensation: Calculate the temperature influence function f_T(T_s) based on real-time T_s;

[0068] Comprehensive calculation: CEF = (P_out / P_in) × (1 - |T_e - T_opt| / T_range) × f_T(T_s).

[0069] T_opt is the optimal torque at the current speed ω determined based on the motor efficiency MAP and / or online learning. Method 1 is a static query based on a preset motor efficiency MAP.

[0070] The system pre-stores an efficiency MAP (lookup table) obtained through motor bench testing. During runtime, the system directly queries the MAP based on the real-time speed ω and obtains the corresponding T_opt through table lookup and interpolation calculation.

[0071] Method 2: Online learning based on real-time data;

[0072] Under stable operating conditions, the system collects real-time operating data (ω,T_e,η), fits the local efficiency-torque curve through an online parameter identification algorithm (recursive least squares method), and directly calculates T_opt corresponding to the extreme point of the curve without relying on a preset MAP diagram.

[0073] Method 3: Decision-making on the integration of MAP diagrams and online learning;

[0074] The system obtains T_opt_map and T_opt_learn in parallel through method 1 and method 2 respectively. The final T_opt is generated by fusing the two according to the confidence weight λ: T_opt = λ·T_opt_learn + (1-λ)·T_opt_map, where the weight λ can be dynamically adjusted according to data quality and learning progress.

[0075] Furthermore, the temperature influence function f_T(T_s) is calculated as follows:

[0076] When T_s≤T_ref, f_T(T_s)=1;

[0077] When T_s>T_ref, f_T(T_s)=1-k×(T_s-T_ref) / (T_max-T_ref);

[0078] Where T_ref is the reference temperature, T_max is the maximum allowable temperature, and k is the temperature influence coefficient.

[0079] Furthermore, the health assessment module calculates the system's health factor H in the following way:

[0080] H = 1 / (1 + D_total);

[0081] Where D_total=α×|I / I_nom|+β×|T_s / T_max|+γ×|(dω / dt) / ω_max|, I_nom is the rated current of the motor, T_max is the maximum allowable temperature corresponding to the insulation class of the motor, ω_max is the maximum allowable speed of the motor, dω / dt is the speed change rate, α, β, and γ are weighting coefficients, and α+β+γ=1.

[0082] Furthermore, the weighting coefficients α, β, and γ are calculated as follows:

[0083] When D_total≠0, α=|I / I_nom| / D_total,β=|T_s / T_max| / D_total,γ=|(dω / dt) / ω_max| / D_total;

[0084] When D_total=0, α=β=γ=1 / 3.

[0085] Temperature influence coefficient k: 0.1~0.5, preferably 0.3;

[0086] Reference temperature T_ref: 70℃~90℃, based on insulation class;

[0087] Weight coefficient initialization: α=0.4, β=0.4, γ=0.2;

[0088] Status level threshold: can be adjusted according to specific application scenarios.

[0089] Furthermore, the state assessment module calculates the overall operating state index S in the following way:

[0090] S = CEF × H;

[0091] The operating status levels are divided into:

[0092] Excellent state: S≥0.8;

[0093] Good condition: 0.6 ≤ S < 0.8;

[0094] General condition: 0.4 ≤ S < 0.6;

[0095] Differential state: S<0.4.

[0096] Furthermore, the optimized execution module includes:

[0097] The decision-making unit selects optimization strategies based on the operational status level and generates control commands;

[0098] The execution unit converts control commands into drive signals to control the operation of the motor.

[0099] The feedback data acquisition unit acquires new system status data after the control command is executed, uses it as feedback data, and sends it to the parameter update module. The feedback data includes the three-phase current, speed, output torque, winding temperature, and DC bus voltage after execution.

[0100] Furthermore, the decision-making unit selects an optimization strategy based on the operational status level:

[0101] Superior state: Maintain current control parameters and make fine adjustments to maintain high efficiency;

[0102] Good condition: Adjust the right-angle axis current distribution to improve efficiency;

[0103] Normal state: Adjust control parameters and strengthen monitoring and early warning;

[0104] Poor condition: Implement reduced operating capacity or shutdown protection measures.

[0105] Specific control parameters corresponding to each operating status level:

[0106] Excellent condition: Direct-axis current adjustment range ±2%, quadrature-axis current remains unchanged;

[0107] Ideal condition: Direct-axis current adjustment range ±5%, quadrature-axis current fine adjustment ±3%;

[0108] Normal state: Direct axis current adjustment range ±10%, quadrature axis current adjustment ±8%;

[0109] Inferior state: Output power is limited to 60% of the rated value, and speed is limited to 80%.

[0110] Furthermore, the parameter update module includes:

[0111] The model parameter correction unit receives feedback data from the optimization execution module, and updates the optimal torque parameter T_opt in the efficiency feature module iteratively using the least squares method based on the rotational speed ω and output torque T_e in the feedback data; and adaptively adjusts the temperature influence coefficient k in the temperature influence function based on the winding temperature T_s in the feedback data.

[0112] The specific implementation of the least squares iterative update of T_opt:

[0113] 1. Establish torque-efficiency observation data pairs (T_e,η);

[0114] 2. Construct a quadratic efficiency model: η = a·T_e² + b·T_e + c;

[0115] 3. Update the model parameters a, b, c using the recursive least squares (RLS) method;

[0116] 4. Find the efficiency extreme point corresponding to T_opt = -b / (2a);

[0117] 5. Set update conditions: Trigger an update when the deviation between the new data and the model prediction is >5%.

[0118] The evaluation benchmark update unit receives feedback data from the optimization execution module, records and analyzes the extreme values ​​of current, temperature and speed in the historical operating data, and updates the numerical benchmarks of rated current I_nom, maximum allowable temperature T_max and maximum allowable speed ω_max used in the health evaluation module for calculation accordingly.

[0119] Furthermore, the data acquisition module synchronously acquires all parameters at a sampling frequency of not less than 10kHz, and includes a timestamp identifier.

[0120] The assessment benchmark update strategy:

[0121] 1. I_nom update: based on the 95th percentile of 1000 consecutive hours of running data;

[0122] 2. T_max update: Sliding window statistics based on seasonal temperature changes;

[0123] 3. ω_max update: Predictive adjustments based on the mechanical component wear model;

[0124] 4. Update frequency: Evaluate every 24 hours, and perform an update when the change exceeds 3%.

[0125] System anomaly handling:

[0126] Sensor fault detection: A three-out-of-two voting mechanism is used to automatically isolate abnormal sensor data;

[0127] Communication interruption handling: Enable local cache control policy to maintain basic operation for at least 30 minutes;

[0128] Parameter anomaly protection: When T_opt is detected to be outside the safe range, it automatically reverts to the default MAP graph.

[0129] Emergency shutdown procedure: When S < 0.2, immediately execute a safe shutdown and record the operating data for 10 seconds before and after the fault.

[0130] In practical use, based on existing technology, after the system starts up, it automatically checks all sensors, loads motor parameters and historical data, and completes system preparation. The system continuously collects key data such as current, speed, and temperature; and calculates two core indicators in real time: "comprehensive efficiency characteristic value" (reflecting the current energy efficiency level) and "system health factor" (reflecting the equipment safety status).

[0131] These two indicators are combined to obtain an "Overall Operating Status Indicator S," which automatically classifies the system status into four levels: Excellent, Good, Average, and Poor. Based on the status level, the system automatically switches to the corresponding optimization strategy: Excellent / Good status: Fine-tuning or optimization of parameters to maintain or improve operating efficiency;

[0132] Normal state: Actively adjust parameters and strengthen monitoring, and issue early warnings;

[0133] Poor condition: Take protective measures such as reducing operating capacity or safe shutdown to ensure equipment safety.

[0134] The system uses the effect data after executing control commands as feedback to dynamically update internal key parameters, enabling the system to continuously learn and optimize itself. Over time, its control strategy becomes more and more accurate, gradually approaching the optimal working state of the motor. This achieves a complete automated closed loop of "perception-diagnosis-decision-learning," upgrading the motor control system from a static, passive operating mode to a dynamic, proactive, and continuously self-optimizing intelligent mode.

[0135] The above description is a further detailed explanation of the present invention in conjunction with specific embodiments, and it should not be considered that the specific implementation of the present invention is limited to this. For those skilled in the art to which the present invention pertains and related fields, any extensions, operation methods, and data substitutions made based on the technical solution concept of the present invention should fall within the protection scope of the present invention.

Claims

1. A dynamic efficiency optimization and control system for electric motors, characterized in that, It includes a data acquisition module, an efficiency characteristic module, a health assessment module and a status assessment module, an optimization execution module and a parameter update module. The data acquisition module collects the three-phase current I, speed ω, output torque T_e, winding temperature T_s and DC bus voltage U_dc of the motor in real time. The efficiency feature module is connected to the data acquisition module and calculates the comprehensive efficiency feature value CEF based on the acquired I, ω, T_e, T_s and U_dc. The health assessment module is connected to the data acquisition module and calculates the system health factor H based on the acquired I, ω, T_e, T_s and U_dc. The status assessment module is connected to the efficiency feature module and the health assessment module. Based on the comprehensive efficiency feature value CEF and the system health factor H, it calculates the overall operating status index S and classifies the operating status level according to the S value. The optimization execution module is connected to the status evaluation module, generates optimization control instructions based on the running status level, executes the control instructions and collects feedback data; The parameter update module is connected to the optimization execution module and is used to receive and process the feedback data, and dynamically update the key parameters in the efficiency feature module and the health assessment module. The parameter update module includes: The model parameter correction unit receives feedback data from the optimization execution module, and updates the optimal torque parameter T_opt in the efficiency feature module iteratively using the least squares method based on the rotational speed ω and output torque T_e in the feedback data; and adaptively adjusts the temperature influence coefficient k in the temperature influence function based on the winding temperature T_s in the feedback data. The evaluation benchmark update unit receives feedback data from the optimization execution module, records and analyzes the extreme values ​​of current, temperature and speed in the historical operating data, and updates the numerical benchmarks of rated current I_nom, maximum allowable temperature T_max and maximum allowable speed ω_max used in the health evaluation module for calculation accordingly.

2. The electric motor dynamic efficiency optimization and control system according to claim 1, characterized in that, The efficiency feature module calculates the comprehensive efficiency feature value (CEF) in the following way: CEF=(P_out / P_in)×(1-|T_e-T_opt| / T_range)×f_T(T_s); Where P_out=ω×T_e represents the output power; P_in=U_dc×I_avg represents the input power, and I_avg is the effective value of the three-phase current I; T_opt is the optimal torque at the current speed ω determined according to the motor efficiency MAP and / or online learning; T_range is the allowable torque range determined according to the motor performance; and f_T(T_s) is the temperature effect function.

3. The electric motor dynamic efficiency optimization and control system according to claim 2, characterized in that, The temperature effect function f_T(T_s) is calculated as follows: When T_s≤T_ref, f_T(T_s)=1; When T_s>T_ref, f_T(T_s)=1-k×(T_s-T_ref) / (T_max-T_ref); Where T_ref is the reference temperature, T_max is the maximum allowable temperature, and k is the temperature influence coefficient.

4. The electric motor dynamic efficiency optimization and control system according to claim 1, characterized in that, The health assessment module calculates the system's health factor H in the following way: H = 1 / (1 + D_total); Where D_total=α×|I / I_nom|+β×|T_s / T_max|+γ×|(dω / dt) / ω_max|, I_nom is the rated current of the motor, T_max is the maximum allowable temperature corresponding to the insulation class of the motor, ω_max is the maximum allowable speed of the motor, dω / dt is the speed change rate, α, β, and γ are weighting coefficients, and α+β+γ=1.

5. The electric motor dynamic efficiency optimization and control system according to claim 4, characterized in that, The weighting coefficients α, β, and γ are calculated as follows: when D_total ≠ 0, α = |I / I_nom| / D_total, β = |T_s / T_max| / D_total, γ = |(dω / dt) / ω_max| / D_total; when D_total = 0, α = β = γ = 1 / 3.

6. The electric motor dynamic efficiency optimization and control system according to claim 1, characterized in that, The state assessment module calculates the overall operating state index S in the following way: S = CEF × H; The operating status levels are divided into: Excellent state: S≥0.8; Good condition: 0.6 ≤ S < 0.8; General condition: 0.4 ≤ S < 0.6; Differential state: S<0.

4.

7. The electric motor dynamic efficiency optimization and control system according to claim 1, characterized in that, The optimized execution module includes: The decision-making unit selects optimization strategies based on the operational status level and generates control commands; The execution unit converts control commands into drive signals to control the operation of the motor. The feedback data acquisition unit acquires new system status data after the control command is executed, uses it as feedback data, and sends it to the parameter update module. The feedback data includes the three-phase current, speed, output torque, winding temperature, and DC bus voltage after execution.

8. The electric motor dynamic efficiency optimization and control system according to claim 7, characterized in that, The decision-making unit selects an optimization strategy based on the operational status level: Superior state: Maintain current control parameters and make fine adjustments to maintain high efficiency; Good condition: Adjust the right-angle axis current distribution to improve efficiency; Normal state: Adjust control parameters and strengthen monitoring and early warning; Poor condition: Implement reduced operating capacity or shutdown protection measures.

9. The electric motor dynamic efficiency optimization and control system according to claim 1, characterized in that, The data acquisition module synchronously acquires all parameters at a sampling frequency of no less than 10kHz, and includes a timestamp identifier.

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