Well repair pump truck electric drive thermal management method and system

Abstract

The invention relates to the technical field of electric workover rig thermal management, and discloses a workover pump truck electric drive thermal management method and system, and the system comprises a multi-source temperature collection module, an operation state monitoring module, a thermal management analysis module, a strategy matching engine module, a dynamic execution control module, a feedback optimization module, and a prediction decision module. The method comprises the following steps: acquiring environment temperature, motor temperature, battery temperature and cooling liquid temperature data; obtaining motor power, battery current and a vehicle operation mode; generating a thermal management demand level based on the temperature and the operating parameters; matching natural cooling, air cooling or liquid cooling strategies according to grades; cooling operation is executed, the temperature change rate is calculated in real time, and the cooling intensity is dynamically adjusted; when the threshold value is exceeded, liquid cooling cooperative control is started; predicting a thermal load trend through an LSTM model, generating a dynamic cooling plan, and optimizing an execution priority; and a thermal management efficiency evaluation report is output, so that the operation reliability and energy efficiency of the electrically-driven workover pump truck are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management of electric workover rigs, and in particular to a thermal management method and system for an electric drive of a workover pump truck. Background Art

[0002] Thermal management technology for electric well repair rigs is an advanced thermal management technology designed to solve the problem of heat generated during motor operation. Motor heat pipe technology mainly utilizes the efficient thermal conductivity of heat pipes to quickly transfer the heat generated inside the motor to an external heat sink, thereby achieving rapid heat dissipation. The working fluid inside the heat pipe will quickly vaporize after being heated, rise to the top of the heat pipe, and then release the heat to the external environment through the condenser. This phase change heat transfer method gives the heat pipe extremely high thermal conductivity and can solve the problem of motor overheating. With the continuous development of motor technology, motor heat pipe technology plays an increasingly important role in improving motor performance and extending service life. It is suitable for multi-modal temperature coordinated control of hybrid and pure electric well repair pump trucks.

[0003] However, the bottlenecks of existing technologies are as follows:

[0004] 1. Thermal management requirements of electric drive systems:

[0005] Modern workover pump truck electric drive systems incorporate high-power permanent magnet synchronous motors, high-energy-density lithium-ion batteries, and power electronics. These systems generate continuous heat loads during operation. Motor winding temperatures exceeding 180°C and battery temperatures exceeding 60°C can trigger irreversible damage, such as magnetic demagnetization and electrolyte decomposition. Traditional air cooling solutions are no longer sufficient for these extreme operating conditions.

[0006] 2. Defects of existing thermal management technology:

[0007] (1) Response hysteresis: Chinese patent publication number CN112145351A discloses a cooling system for construction machinery based on a water temperature threshold. The cooling pump is activated only when the coolant temperature reaches a fixed threshold. Actual measurement data shows that when operating in a desert environment, it takes 90 seconds for the motor temperature to rise from 70°C to the critical temperature. This system's response delay increases the risk of overheating.

[0008] (2) Energy efficiency imbalance: The patent with announcement number CN113700528B adopts a continuous high-power air cooling solution. In the intermittent operation mode of the well repair pump truck, the energy consumption of the cooling system accounts for 28% of the energy consumption of the entire vehicle, shortening the cruising range.

[0009] (3) Single strategy: Existing technologies do not differentiate between vehicle operating modes and still maintain high power heat dissipation during transitions, resulting in energy waste.

[0010] 3. Focus of technical contradictions

[0011] (1) The contradiction between the dynamic change of heat dissipation demand of electric drive components and fixed threshold control;

[0012] (2) The contradiction between the need for collaborative heat dissipation from multiple heat sources and the traditional discrete cooling architecture;

[0013] (3) The difficulty of balancing thermal management energy consumption optimization and system safety.

[0014] To address these challenges, the present invention has developed an intelligent thermal management system that integrates real-time thermal state sensing, multi-stage cooling strategy matching, and thermal load trend prediction. This represents a key breakthrough in improving the operational reliability and energy efficiency of electric-driven well-servicing pump trucks. This invention aims to overcome the triple bottlenecks of existing technologies—slow response, excessive energy consumption, and rigid strategies—and establish a new paradigm for closed-loop thermal management that is adaptive to operating conditions. Summary of the Invention

[0015] (1) Technical problems solved

[0016] In view of the shortcomings of the existing technology, the present invention provides a method and system for thermal management of an electric drive of a well repair pump truck, which solves the problems raised in the above background technology.

[0017] (2) Technical solution

[0018] To achieve the above objectives, the present invention provides the following technical solutions: a method and system for thermal management of an electric drive of a well repair pump truck, comprising the following steps:

[0019] S1. Collecting temperature data of the electric drive system of the workover pump truck, wherein the temperature data includes ambient temperature data, motor temperature data, battery temperature data, and coolant temperature data;

[0020] S2. Acquire operating status parameters of the well workover pump truck, wherein the operating status parameters include motor power data, battery output current data, and vehicle operating mode data;

[0021] S3. Performing a thermal management demand analysis based on the temperature data and operating status parameters to generate thermal management demand level data;

[0022] S4. Matching a corresponding cooling strategy according to the thermal management requirement level data to generate target cooling strategy type data, where the cooling strategy includes a natural cooling strategy, an air cooling strategy, and a liquid cooling strategy;

[0023] S5. Generate a cooling control instruction based on the target cooling strategy type data, and control the cooling system to perform a corresponding cooling operation;

[0024] S6. monitoring temperature changes of the electric drive system in real time, and adjusting the cooling strategy based on the temperature change feedback data;

[0025] S7: When the thermal management demand level exceeds the preset threshold, a multi-stage linkage cooling strategy is activated, integrating air cooling and liquid cooling to work together;

[0026] S8. Predict future heat load trends based on historical temperature data and vehicle operating patterns and generate dynamic cooling plans;

[0027] S9. Optimizing the execution priority of the cooling control instructions according to the dynamic cooling plan;

[0028] S10. Output thermal management performance evaluation report, including energy consumption statistics, temperature stability indicators and cooling response time data.

[0029] Preferably, according to the method and system for thermal management of an electric drive of a well workover pump truck according to claim 1, S1 includes:

[0030] S11, collecting ambient temperature data through a distributed temperature sensor network, wherein the sensors are arranged on the chassis, motor compartment, and battery compartment of the well repair pump truck;

[0031] S12. Collect motor stator winding temperature data and battery cell temperature data through embedded thermocouples, with a sampling frequency of 10 Hz;

[0032] S13. Collect coolant inlet temperature data and outlet temperature data through a digital temperature probe in the liquid cooling circuit, and calculate the temperature gradient value.

[0033] Preferably, according to the method and system for thermal management of an electric drive of a well workover pump truck according to claim 2, S2 includes:

[0034] S21. Reading motor power data in real time via the vehicle CAN bus, the power data including instantaneous power and average power;

[0035] S22. Obtain battery output current data and charge / discharge rate through a battery management system;

[0036] S23. Determine vehicle operation mode data based on the onboard controller, including idle mode, working mode, and transition mode.

[0037] Preferably, according to the method and system for thermal management of an electric drive of a well workover pump truck according to claim 3, S3 includes:

[0038] S31. Construct heat load assessment matrix:

[0039] Q=α·T 电机 +β·T 电池 +γ·ΔT 冷却液

[0040] Among them, α, β, and γ are weight coefficients, which are dynamically adjusted according to the operation mode;

[0041] S32. Use fuzzy logic algorithm to map the heat load value into thermal management demand level data, including four levels: low load, medium load, high load and emergency load.

[0042] Preferably, according to the method and system for thermal management of an electric drive of a well workover pump truck according to claim 4, S4 includes:

[0043] S41. Establish a cooling strategy matching rule base to store cooling strategy combinations corresponding to different thermal management requirement levels;

[0044] S42: When the thermal management requirement level is low load, the natural cooling strategy is matched and passive cooling using heat sink fins is enabled;

[0045] S43: When the thermal management requirement level is medium load, the air cooling strategy is matched, the variable frequency fan is started and the speed is adjusted;

[0046] S44: When the thermal management requirement level is high load or emergency load, the liquid cooling strategy is matched to control the electronic water pump flow and compressor power.

[0047] Preferably, according to the method and system for thermal management of an electric drive of a well workover pump truck according to claim 5, S5 includes:

[0048] S51, converting the target cooling strategy type data into a PWM control signal;

[0049] S52, controlling the speed of the variable frequency fan of the air cooling module through the drive circuit, and adjusting the speed;

[0050] S53. Regulate the coolant flow of the liquid cooling module through a proportional valve to control the flow accuracy.

[0051] Preferably, according to the method and system for thermal management of an electric drive of a well workover pump truck according to claim 6, S6 includes:

[0052] S61. Calculate the temperature change rate:

[0053]

[0054] Where ΔR is the temperature change rate, is the differential change of temperature, is the differential change of time;

[0055] S62, when ΔR exceeds a preset threshold, a cooling strategy adjustment coefficient is generated based on a PID control algorithm;

[0056] S63. Dynamically correct the air cooling fan speed or liquid cooling flow rate setting value according to the adjustment coefficient.

[0057] Preferably, according to the electric drive thermal management method and system for a well workover pump truck according to claim 1, S8 includes:

[0058] S81. Build an LSTM neural network model with input parameters including historical temperature series, motor power change trend, and ambient temperature prediction value;

[0059] S82. Outputting a heat load trend curve for the next 10 minutes through the model;

[0060] S83. Generate a dynamic cooling plan based on the heat load trend curve, including preheating start, staged pressurization, and emergency load reduction strategies.

[0061] Preferably, according to the electric drive thermal management method for a well repair pump truck according to claim 1, S9 comprises the following steps:

[0062] S91. Determine the priority order of cooling instruction execution according to the predicted peak time point of the heat load in the dynamic cooling plan;

[0063] S92. When multiple cooling instructions conflict in time, emergency load reduction instructions related to safety are executed first;

[0064] S93. Under non-emergency conditions, the cooling strategy is executed in the order of preheating and starting, followed by staged supercharging, to optimize energy consumption.

[0065] Preferably, according to the computer-readable storage medium according to claim 10, when the program is executed by the processor to implement the step S10, the program specifically includes the following operations:

[0066] S101, calculating and counting the total energy consumption of the cooling system, and accumulating the real-time power consumption of the liquid cooling subsystem and the air cooling subsystem during the operation cycle through integral operation;

[0067] S102. Quantify the temperature stability index, evaluate the system temperature control capability by calculating the integrated mean of the temperature change rate, and generate a stability rating;

[0068] S103, recording and analyzing cooling response time data, including the time delay from instruction generation to execution and the stabilization time required from triggering cooling to reaching the temperature target;

[0069] S104. Generate a visual thermal management performance evaluation report, including an energy consumption distribution analysis chart, a temperature fluctuation curve chart, and a response time comparison chart, and mark abnormal operating points;

[0070] S105. Automatically generate system optimization suggestions and maintenance warning prompts based on the temperature stability indicators and energy consumption data in the evaluation report.

[0071] (3) Beneficial effects

[0072] Compared with the prior art, the present invention provides a method and system for thermal management of an electric drive of a well repair pump truck, which has the following beneficial effects:

[0073] 1. In the present invention, the temperature change rate is calculated in real time A multi-level threshold judgment rule is also established to predict heat dissipation anomalies before the temperature reaches the absolute threshold. The cooling intensity coefficient K is dynamically adjusted in combination with the PID algorithm, shortening the system response time compared to traditional solutions, improving the response speed, and solving the risks of motor demagnetization and battery thermal runaway under high-temperature conditions.

[0074] 2. In the present invention, through energy efficiency optimization breakthrough, based on the heat load evaluation matrix:

[0075] Q=α·T 电机 +β·T 电池 +γ·ΔT 冷却液

[0076] Among them, α, β, and γ are weight coefficients;

[0077] Dynamically adjust according to the operating mode, generate the demand level, and dynamically match the cooling strategy with the vehicle operating mode: Idle mode: enable natural cooling; Transition mode: air cooling speed decays exponentially:

[0078] rpm=2000×e -0.05t

[0079] Among them, 2000 is the initial air cooling speed of the transition mode, t is the cumulative time after the transition is started, e x Simulates the natural attenuation characteristics; Operation mode: Liquid cooling flow is adjusted linearly:

[0080] F=K p ΔR

[0081] Among them, F is the coolant circulation flow rate, K p is the proportional control gain coefficient, ΔR is the rate of change of temperature with time, and the energy consumption is lower than that of the traditional solution, saving 14L of diesel per day.

[0082] 3. This invention achieves breakthroughs in adaptability to all operating conditions and integrates three innovative mechanisms: Predictive control: The LSTM model inputs the historical temperature sequence [Tt-5, Tt-4, ..., Tt] and outputs the heat load trend curve Tpredict for the next 10 minutes; Multi-level linkage: The air-cooling-liquid-cooling synergy equation is activated under emergency load:

[0083] P 总 =0.7P 液冷 +0.3P 风冷

[0084] Among them, P总 is the total cooling power, P 液冷 is the power of the liquid cooling subsystem, P 风冷 Power of the air-cooled subsystem; Low temperature adaptation: -30℃ environment according to T 预热 =25·(1-e -0.2t ) Exponential curve preheats the battery to make the system run stably and reduce the failure rate.

[0085] 4. In the present invention, component life prediction is automatically generated through the thermal management performance evaluation report: fan life:

[0086]

[0087] Among them, L 风 is the life of the fan, 20000 is the life reference constant, rpm(τ) is the speed function, dτ is the time differential, t is the upper limit of time, and 0 is the lower limit of time; battery decay rate:

[0088] η=0.01·max(T 电池 -45,0)

[0089] Among them, η is the battery attenuation rate, 0.01 is the attenuation reference coefficient, max(x,0) is the temperature threshold function, T 电池 is the battery temperature, and 45 is the temperature threshold, which guides preventive maintenance and reduces maintenance costs.

[0090] Through the triple technological innovations of differential dynamic monitoring, fuzzy demand classification, and predictive control, this invention achieves simultaneous breakthroughs in response speed, energy efficiency ratio, and operating condition adaptability, solving the industry pain points of traditional thermal management technology, such as serious lag, excessive energy consumption, and rigid strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 Schematic diagram of the method steps of the present invention;

[0092] Figure 2 It is a schematic diagram of the overall system architecture of the present invention. DETAILED DESCRIPTION

[0093] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0094] See also Figure 1 The electric drive thermal management method and system for a well repair pump truck include the following steps:

[0095] S1. Collect temperature data of the electric drive system of the workover pump truck, including ambient temperature data, motor temperature data, battery temperature data, and coolant temperature data;

[0096] S2. Acquire operating status parameters of the well workover pump truck, including motor power data, battery output current data, and vehicle operating mode data;

[0097] S3. Perform thermal management demand analysis based on temperature data and operating status parameters to generate thermal management demand level data;

[0098] S4. Match the corresponding cooling strategy according to the thermal management requirement level data and generate target cooling strategy type data. The cooling strategies include natural cooling strategy, air cooling strategy, and liquid cooling strategy.

[0099] S5. Generate a cooling control instruction based on the target cooling strategy type data, and control the cooling system to perform a corresponding cooling operation;

[0100] S6. Real-time monitoring of temperature changes in the electric drive system and adjustment of the cooling strategy based on temperature change feedback data;

[0101] S7: When the thermal management demand level exceeds the preset threshold, a multi-stage linkage cooling strategy is activated, integrating air cooling and liquid cooling to work together;

[0102] S8. Predict future heat load trends based on historical temperature data and vehicle operating patterns and generate dynamic cooling plans;

[0103] S9. Optimizing the execution priority of cooling control instructions according to the dynamic cooling plan;

[0104] S10. Output thermal management performance evaluation report, including energy consumption statistics, temperature stability indicators and cooling response time data;

[0105] S11. Collect ambient temperature data through a distributed temperature sensor network, with the sensors deployed on the chassis, motor compartment, and battery compartment of the workover pump truck;

[0106] S12. Collect motor stator winding temperature data and battery cell temperature data through embedded thermocouples, with a sampling frequency of 10 Hz;

[0107] S13, collecting coolant inlet temperature data and outlet temperature data through a digital temperature probe in the liquid cooling circuit, and calculating a temperature gradient value;

[0108] S21. Read motor power data in real time through the vehicle CAN bus. The power data includes instantaneous power and average power.

[0109] S22. Obtain battery output current data and charge / discharge rate through a battery management system;

[0110] S23, determining vehicle operation mode data based on the onboard controller, including idle mode, working mode, and transition mode;

[0111] S31. Construct heat load assessment matrix:

[0112] Q=α·T 电机 +β·T 电池 +γ·ΔT 冷却液

[0113] Among them, α, β, and γ are weight coefficients, which are dynamically adjusted according to the operation mode;

[0114] S32, using a fuzzy logic algorithm to map the heat load value into thermal management demand level data, including four levels: low load, medium load, high load, and emergency load;

[0115] S41. Establish a cooling strategy matching rule base to store cooling strategy combinations corresponding to different thermal management requirement levels;

[0116] S42: When the thermal management requirement level is low load, the natural cooling strategy is matched and passive cooling using heat sink fins is enabled;

[0117] S43: When the thermal management requirement level is medium load, the air cooling strategy is matched, the variable frequency fan is started and the speed is adjusted;

[0118] S44: When the thermal management requirement level is high load or emergency load, the liquid cooling strategy is matched to control the electronic water pump flow and compressor power;

[0119] S51, converting the target cooling strategy type data into a PWM control signal;

[0120] S52, controlling the speed of the variable frequency fan of the air cooling module through the drive circuit, and adjusting the speed;

[0121] S53, regulating the coolant flow of the liquid cooling module through a proportional valve to control the flow accuracy;

[0122] S61. Calculate the temperature change rate:

[0123]

[0124] Where ΔR is the temperature change rate, is the differential change of temperature, is the differential change of time;

[0125] S62, when ΔR exceeds a preset threshold, a cooling strategy adjustment coefficient is generated based on a PID control algorithm;

[0126] S63, dynamically correcting the air cooling fan speed or liquid cooling flow setting value according to the adjustment coefficient;

[0127] S81. Build an LSTM neural network model with input parameters including historical temperature series, motor power change trend, and ambient temperature prediction value;

[0128] S82. Outputting a heat load trend curve for the next 10 minutes through the model;

[0129] S83. Generate dynamic cooling plans based on the heat load trend curve, including preheating start, staged pressurization, and emergency load reduction strategies;

[0130] S91. Determine the priority order of cooling instruction execution according to the predicted peak time point of the heat load in the dynamic cooling plan;

[0131] S92: When there is a time conflict between multiple cooling instructions, the emergency load reduction instruction related to safety shall be executed first;

[0132] S93. Under non-emergency conditions, the cooling strategy is executed in the order of preheating and staged supercharging to optimize energy consumption.

[0133] S101, calculating and counting the total energy consumption of the cooling system, and accumulating the real-time power consumption of the liquid cooling subsystem and the air cooling subsystem during the operation cycle through integral operation;

[0134] S102. Quantify the temperature stability index, evaluate the system temperature control capability by calculating the integrated mean of the temperature change rate, and generate a stability rating;

[0135] S103, recording and analyzing cooling response time data, including the time delay from instruction generation to execution and the stabilization time required from triggering cooling to reaching the temperature target;

[0136] S104. Generate a visual thermal management performance evaluation report, including an energy consumption distribution analysis chart, a temperature fluctuation curve chart, and a response time comparison chart, and mark abnormal operating points;

[0137] S105. Automatically generate system optimization suggestions and maintenance warning prompts based on the temperature stability indicators and energy consumption data in the evaluation report.

[0138] Specific Example 1: Continuous Operation at High Temperature in Desert

[0139] Scenario description: An XJ-750 workover pump truck in the Tarim Oilfield performs fracturing operations in a high-temperature environment of 48°C. The motor power continuously reaches 280kW, causing the battery cell temperature to rise.

[0140] Thermal Management Response Process: Heat Load Matrix Calculation:

[0141] Q=0.6×182+0.3×58+0.1×18=138.8

[0142] Among them, the weight α=0.6, β=0.3, γ=0.1, triggering the emergency load level; starting the air cooling-liquid cooling collaborative strategy: Liquid cooling power: P 消冷 = 0.7 × 50 = 35 kW, air cooling speed 5000 rpm, the LSTM model predicts that the heat load will exceed 150 in 10 minutes, and the liquid cooling boost mode is activated in advance.

[0143] Implementation effect: Motor temperature is reduced, temperature rise rate is reduced, energy consumption is only 31kWh, and battery attenuation rate η is controlled at 0.13% / h; 12 hours of operation interruption due to overheating is avoided, creating an economic benefit of 230,000 yuan.

[0144] Specific Example 2: Transfer Operation in Cold Areas

[0145] Scenario description: In the extremely cold environment of -25℃ in Daqing Oilfield, a workover pump truck completes wellhead maintenance and moves to a new well site 20 kilometers away, causing the battery temperature to drop.

[0146] Thermal management response process: The operating mode is identified as transition mode, the air cooling speed decays exponentially according to rpm = 2000 × e-0.05t, and the low-temperature preheating strategy is activated: the battery pack is heated according to the T preheat = 25 × (1-e-0.2t) curve. The thermal management requirement maintains a low load, and only the heat sink fins are turned on for passive cooling.

[0147] Implementation effect: The battery temperature rose to 5°C within 15 minutes, the power consumption was only 0.8kWh, the fan life loss was reduced, and the risk of damage to the 130,000 yuan battery pack caused by low-temperature electrolyte solidification was eliminated.

[0148] Specific Example 3: Intermittent Operation on Offshore Platform

[0149] Scenario description: A workover pump truck on a Bohai offshore platform performs intermittent well flushing operations. The ambient humidity of 98% accelerates component corrosion.

[0150] Thermal management response process: The LSTM model is trained based on historical data to accurately predict intermittent heat load fluctuation cycles. Natural cooling is automatically switched to during the standby phase, and the liquid cooling flow rate is reduced to Fmin = 0.5L / min. The anti-condensation strategy is activated: the fan maintains a minimum speed of 800rpm to ensure air circulation.

[0151] Implementation results: Energy savings, saving 24L of diesel per day; electronic water pump life extended to 19,000 hours; mainboard condensation failure rate reduced.

Claims

1. A method and system for thermal management of an electric drive of a well repair pump truck, characterized by: The following steps are involved: S1. Collecting temperature data of the electric drive system of the workover pump truck, wherein the temperature data includes ambient temperature data, motor temperature data, battery temperature data, and coolant temperature data; S2. Acquire operating status parameters of the well workover pump truck, wherein the operating status parameters include motor power data, battery output current data, and vehicle operating mode data; S3. Performing a thermal management demand analysis based on the temperature data and operating status parameters to generate thermal management demand level data; S4. Matching a corresponding cooling strategy according to the thermal management requirement level data to generate target cooling strategy type data, where the cooling strategy includes a natural cooling strategy, an air cooling strategy, and a liquid cooling strategy; S5. Generate a cooling control instruction based on the target cooling strategy type data, and control the cooling system to perform a corresponding cooling operation; S6. monitoring temperature changes of the electric drive system in real time, and adjusting the cooling strategy based on the temperature change feedback data; S7: When the thermal management demand level exceeds the preset threshold, a multi-stage linkage cooling strategy is activated, integrating air cooling and liquid cooling to work together; S8. Predict future heat load trends based on historical temperature data and vehicle operating patterns and generate dynamic cooling plans; S9. Optimizing the execution priority of the cooling control instructions according to the dynamic cooling plan; S10. Output thermal management performance evaluation report, including energy consumption statistics, temperature stability indicators and cooling response time data.

2. The electric drive thermal management method and system for a well repair pump truck according to claim 1, characterized in that: Said S1 comprises: S11, collecting ambient temperature data through a distributed temperature sensor network, wherein the sensors are arranged on the chassis, motor compartment, and battery compartment of the well repair pump truck; S12. Collect motor stator winding temperature data and battery cell temperature data through embedded thermocouples, with a sampling frequency of 10 Hz; S13. Collect coolant inlet temperature data and outlet temperature data through a digital temperature probe in the liquid cooling circuit, and calculate the temperature gradient value.

3. The electric drive thermal management method and system for a well repair pump truck according to claim 2, characterized in that: The S2 includes: S21. Reading motor power data in real time via the vehicle CAN bus, the power data including instantaneous power and average power; S22. Obtain battery output current data and charge / discharge rate through a battery management system; S23. Determine vehicle operation mode data based on the onboard controller, including idle mode, working mode, and transition mode.

4. The electric drive thermal management method and system for a well repair pump truck according to claim 3, characterized in that: The S3 includes: S31. Construct heat load assessment matrix: Q=α·T 电机 +β·T 电池 +γ·ΔT 冷却液 Among them, α, β, and γ are weight coefficients, which are dynamically adjusted according to the operation mode; S32. Use fuzzy logic algorithm to map the heat load value into thermal management demand level data, including four levels: low load, medium load, high load and emergency load.

5. The electric drive thermal management method and system for a well repair pump truck according to claim 4, characterized in that: The S4 includes: S41. Establish a cooling strategy matching rule base to store cooling strategy combinations corresponding to different thermal management requirement levels; S42: When the thermal management requirement level is low load, the natural cooling strategy is matched and passive cooling using heat sink fins is enabled; S43: When the thermal management requirement level is medium load, the air cooling strategy is matched, the variable frequency fan is started and the speed is adjusted; S44: When the thermal management requirement level is high load or emergency load, the liquid cooling strategy is matched to control the electronic water pump flow and compressor power.

6. The electric drive thermal management method and system for a well repair pump truck according to claim 5, characterized in that: The S5 includes: S51, converting the target cooling strategy type data into a PWM control signal; S52, controlling the speed of the variable frequency fan of the air cooling module through the drive circuit, and adjusting the speed; S53. Regulate the coolant flow of the liquid cooling module through a proportional valve to control the flow accuracy.

7. The electric drive thermal management method and system for a well repair pump truck according to claim 6, characterized in that: The S6 includes: S61. Calculate the temperature change rate: Where ΔR is the temperature change rate, is the differential change of temperature, is the differential change of time; S62, when ΔR exceeds a preset threshold, a cooling strategy adjustment coefficient is generated based on a PID control algorithm; S63. Dynamically correct the air cooling fan speed or liquid cooling flow rate setting value according to the adjustment coefficient.

8. The electric drive thermal management method and system for a well repair pump truck according to claim 1, characterized in that: The S8 includes: S81. Build an LSTM neural network model with input parameters including historical temperature series, motor power change trend, and ambient temperature prediction value; S82. Outputting a heat load trend curve for the next 10 minutes through the model; S83. Generate a dynamic cooling plan based on the heat load trend curve, including preheating start, staged pressurization, and emergency load reduction strategies.

9. A thermal management system for an electric drive of a well-repair pump truck, used to implement the thermal management method for an electric drive of a well-repair pump truck according to any one of claims 1 to 8, characterized in that: include: Multi-source temperature acquisition module, used to collect ambient temperature, motor temperature, battery temperature and coolant temperature data; Operation status monitoring module, used to obtain motor power, battery current and vehicle operation mode data; Thermal management analysis module, used to generate thermal management demand level data; Strategy matching engine module, used to match target cooling strategy type data; A dynamic execution control module, used for generating and executing cooling control instructions; Feedback optimization module, used to adjust the cooling strategy based on temperature change feedback data; Prediction and decision-making module, used to generate dynamic cooling plans and execute priority instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the electric drive thermal management method for a well repair pump truck according to any one of claims 1 to 8 is implemented.

Images