Control method and system for auxiliary starting heating of engine coolant

Abstract

The invention discloses a control method and system for auxiliary start heating of engine coolant, and aims to solve the problems of difficult cold start, uneven heating and low energy efficiency of an engine in a cold environment. By monitoring the external environment temperature and the cooling liquid temperature in real time, when to start the heating device is intelligently judged, the heating power and time are automatically adjusted according to environment changes, the power of the heater is adjusted through a PID control algorithm, it is ensured that the temperature rising process is stable and controllable, and the temperature is prevented from being too fast or too slow. And it is guaranteed that heated cooling liquid is evenly distributed in the engine through the cooling liquid circulating pump, and local overheating is prevented. Fuzzy logic reasoning and a machine learning algorithm are combined, the heating process is optimized according to historical data, and the energy efficiency and the heating precision are further improved. According to the system, the starting performance of the engine under the low-temperature condition is improved, energy utilization in the heating process is optimized, and high intellectualization and safety are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of engine start control, and in particular to a control method and system for auxiliary start heating of engine coolant. Background Art

[0002] In cold environments, the engine often faces the problem of low coolant temperature during cold start, resulting in the engine being unable to start smoothly. At low temperatures, the engine oil inside the engine becomes thicker and the coolant also becomes viscous, increasing the difficulty of starting. This difficulty in starting in a low temperature environment not only increases fuel consumption, but may also damage the internal components of the engine and shorten the engine life. Traditional cold start heating methods may have the problem of uneven coolant heating, resulting in overheating or insufficient heating of some engine components. Uneven heating of the coolant can cause some engine components (such as cylinders, cylinder heads, etc.) to overheat locally, while other parts are still at low temperatures, which may cause thermal damage, deformation or unbalanced work, affecting the normal operation of the engine. Traditional heating devices may not accurately control the heating power during the temperature rise process, resulting in too fast or too slow heating speed. If the heating process is too fast, it may cause overheating of the coolant, causing excessive energy waste or damage to the system; while heating too slowly may cause the engine to fail to reach the operating temperature in time, affecting the starting efficiency and the operation of the vehicle. Summary of the invention

[0003] A control method for auxiliary start-up heating of engine coolant comprises the following steps; S1. Monitoring ambient temperature: The temperature sensor is activated. When the vehicle is stationary or just started, the ambient temperature sensor (usually installed on the outside of the vehicle) starts working. The system detects the external ambient temperature in real time. If the external temperature is detected to be lower than the set critical value (such as 0°C or -5°C), the low-temperature preheating mode is triggered. Wake-up preheating program: When the ambient temperature is lower than the set value, the preheating system is awakened and ready to heat the coolant; S2. Detect engine coolant temperature: Coolant temperature sensor monitoring: The coolant temperature sensor (usually installed in the engine block or coolant circuit) monitors the current coolant temperature, and the temperature threshold is judged: If the coolant temperature is lower than the minimum temperature required for normal engine operation (for example, lower than 20°C), the heating system enters the next step to prepare for starting, and compares the ambient and coolant temperatures: The system compares the ambient temperature and the coolant temperature to decide whether to continue the coolant heating or directly prepare to start the engine; S3. Start the coolant heating device: The system starts the coolant heating device, usually using a PTC (positive temperature coefficient) heater or a resistance heater, connected to the engine coolant circuit, heater parameter setting: The system calculates the required heating power and time based on the ambient temperature, coolant temperature and power supply status. At low temperatures: If the temperature is low, the system will start the heater with a higher power, usually working at full load. When the temperature approaches the target: As the coolant temperature approaches the predetermined heating target, the system automatically reduces the heater power to avoid overheating; S4. Heating time and power control: During the heating process, the coolant temperature sensor continuously monitors the temperature change of the coolant and uses the PID control algorithm to adjust the power of the heater in real time to make the coolant temperature change smooth and controllable. Combined with the machine learning algorithm, the temperature change pattern is analyzed based on historical data to further optimize the heating time and power control, and the preset target is that the coolant temperature reaches the range of 30°C-50°C; S5. Circulating coolant: During the heating process, the coolant circulation pump starts to work, circulating the heated coolant to various parts of the engine cooling system, ensuring that the temperature of important parts of the engine block and cylinder head gradually rises. The coolant enters the engine block through the pipeline from the heater and then returns to the heater for secondary heating, forming a closed-loop cycle. Through the circulation of the coolant, local overheating is prevented, and the overall temperature of the system is accelerated to rise, so that all parts of the engine are heated evenly; S6. Judgment before engine start: During the heating process, the coolant temperature is continuously detected, based on the current coolant temperature, external environment and the working status of the heating device, and the formula is integrated: , where Tc is the current coolant temperature (°C), Ta is the ambient temperature (°C), S is the working state of the heating device (0 means not working, 1 means working), k is the temperature rise rate coefficient (depends on the power and efficiency of the heating device, in °C / s), t: time (seconds), based on the formula, predict whether the coolant has reached the temperature required for starting, and make a start judgment in advance, and use the fuzzy logic judgment model to judge whether the start conditions are met. When the start conditions are met, the system sends a signal to allow the driver and the automatic control system to start the engine; S7. Stop heating device: After the engine is started, the coolant heater automatically stops working to avoid overheating. The coolant pump continues to work, and the engine maintains its operating temperature through the normal cooling circulation system. The heater enters standby mode and monitors the temperature at any time. When the system temperature drops to the set value again, the heating can be restarted; S8. Fault detection and safety protection: The system will detect the working status of key equipment such as the heater, power supply, coolant circulation pump, etc. to ensure the normal operation of the entire heating system. If the heater is overloaded, the temperature is too high, or the circulation is not smooth (such as a water pump failure), the system will issue an alarm signal and automatically cut off the power supply to prevent further damage. After detecting a fault, the system will take different countermeasures according to the type of fault, such as reducing the heater power, restarting the circulation pump, or directly stopping heating.

[0004] Furthermore, a control method for auxiliary starting heating of engine coolant is provided. In step S4, the PID control algorithm is used to adjust the power of the heater in real time to make the change of the coolant temperature controllable. The machine learning algorithm is combined to analyze the temperature change pattern according to historical data to further optimize the heating time and power control. The specific steps are as follows; S41. Initialize the PID controller: the input is the current coolant temperature and the target temperature, the output is the heater power, set the target coolant temperature range, and input the current coolant temperature as feedback to the PID controller, and adjust the heater power according to the PID calculation result. The power adjustment range should be limited according to the specifications and design of the heater (for example, between 0-100%), while avoiding overheating; S42. Real-time feedback adjustment: monitor the coolant temperature in real time and continuously feed back the current temperature to the PID controller to ensure that the system continuously optimizes the heating process and avoids over-high or under-low temperatures; S43. Combine machine learning algorithms to optimize heating time and power control; S431. Data collection and preprocessing: Collect data from the historical heating process, including ambient temperature, initial coolant temperature, heating power, heating time, and target temperature, clean the data, process outliers and missing values, and standardize and normalize the data to ensure that the data can be effectively trained in the machine learning model; S432. Model training: Use historical data to train a decision tree regression model so that it can understand the temperature variation pattern and heater power adjustment mode during the heating process, select key features that affect the heating process: ambient temperature, current coolant temperature, heater power as model inputs, and optimize the model through cross-validation and hyperparameter adjustment methods; S433. Prediction and optimization: Based on the decision tree regression model, the heating process is predicted. According to the predicted heating process, accurate heating power adjustment suggestions are provided. The best match between temperature rise and power change in historical data is analyzed through machine learning algorithms to optimize the heating process, shorten the heating time, and improve energy efficiency. S434. Real-time feedback and dynamic adjustment: Feedback the output of the decision tree regression model to the PID control system to adjust the heating power in real time. The optimization results of the model provide more accurate adjustment parameters for the PID controller, making the heating process more efficient, and constantly learning and adapting to new environments and working conditions to achieve gradual optimization; S44. Closed-loop system combining PID control and machine learning: Based on the above steps, the PID control algorithm and the decision tree regression model are combined to form a closed-loop system that adjusts the heater power in real time and performs self-optimization based on historical data; S45. Self-learning and improvement: As the system continues to run, the decision tree regression model continuously learns from new heating data and optimizes the heating strategy so that the system can cope with various working conditions and environmental conditions. After the system learns the new mode, the parameters of the PID controller are adjusted according to the feedback from machine learning to improve heating efficiency and accuracy.

[0005] Furthermore, a control method for auxiliary starting heating of engine coolant is provided. In step S6, the temperature of the coolant is continuously detected during the heating process, and the formula is obtained based on the current coolant temperature, the external environment and the working state of the heating device: ,in, Indicates time t The coolant temperature at the moment, Tc is the current coolant temperature, Ta is the external environment temperature, S is the working state of the heating device, k is the temperature rise rate coefficient, t represents time, based on the formula to predict whether the coolant has reached the temperature required for startup, the specific steps are as follows; S61. Parameter definition: current coolant temperature Tc, obtained from the temperature sensor, input range 0°C to 100°C, external ambient temperature Ta, obtained through external temperature sensor, weather API, affecting the rate of increase of coolant temperature, heating device working state S, including heating power, working time, input range: 0 (stop), 1 (working), target temperature Tg, preset optimal coolant temperature required for engine start; S62. Fuzzy set definition: Coolant temperature Tc is defined as "very cold", "cold", "moderate", "warm", "very warm"; ambient temperature Ta is defined as "extremely cold", "cold", "cool", "warm", "hot"; heating device working state S is defined as "inefficient", "moderate", "efficient"; S63. Fuzzy rule making: Rule 1: If the coolant temperature is "very cold" and the ambient temperature is "extremely cold", the start condition is "not met"; Rule 2: If the coolant temperature is "cold" and the ambient temperature is "cold", the start condition "may not be satisfied"; Rule 3: If the coolant temperature is "moderate" and the ambient temperature is "cool", the start condition is "likely to be met"; Rule 4: If the coolant temperature is "warm" and the ambient temperature is "warm", then the start condition is "satisfied" Rule 5: If the coolant temperature is "very warm" and the ambient temperature is "hot", the start condition is "highly satisfied"; S64. Fuzzification of input variables: converting the actually measured coolant temperature, external environment temperature and heating device working state into corresponding fuzzy set membership degrees; S65. Rule evaluation: Evaluate each rule and calculate the fuzzy set membership of the output result of each rule; S66. Output variable defuzzification: Use the weighted average method to combine the output results of multiple rules and integrate them to obtain the formula: , make the final start condition judgment according to the formula; S67. Startup judgment: If the startup condition is "not satisfied" or "may not be satisfied": continue heating and re-evaluate regularly. If the startup condition is "may be satisfied": prompt the user to choose to start and wait for a while before starting. If the startup condition is "satisfied" or "highly satisfied": allow startup and record the relevant data of this startup; S68. Feedback and learning: Get new input data in real time: coolant temperature, ambient temperature, heater status, and continuously adjust the startup judgment according to the above steps. Every time the sensor reads new data, the system needs to re-fuzzify, reason, defuzzify and judge to make a startup decision.

[0006] A control system for auxiliary start-up heating of an engine coolant, the control system for auxiliary start-up heating of an engine coolant is used to implement a control method for auxiliary start-up heating of an engine coolant; the control system for auxiliary start-up heating of an engine coolant comprises: a data acquisition and sensor monitoring module, a fuzzy logic reasoning and PID control module, a heating process optimization and control module, a judgment and decision module, and a safety and fault protection module; The data acquisition and sensor monitoring module collects the ambient temperature, coolant temperature and heating device status in real time through sensors, and sends the data to the fuzzy logic reasoning module and PID control module; Fuzzy logic reasoning and PID control module: The fuzzy logic module determines whether the system can be started based on the ambient temperature, coolant temperature and heater status. The PID controller adjusts the power of the heater in real time to ensure that the coolant temperature rises gradually. Heating process optimization and control module: optimizes heating time and power regulation based on decision tree regression model to ensure efficient and smooth heating process; Judgment and decision module: The system determines whether the starting conditions are met based on fuzzy logic. When the coolant has reached the starting temperature, the engine is started; if not, heating continues; Safety and fault protection module: The system monitors the status of heaters and power supply equipment in real time to ensure safe operation of the system. When a fault occurs, a warning is issued and protective measures are taken.

[0007] The beneficial effects of the present invention are as follows: it is possible to determine whether the heater needs to be started based on the ambient temperature and the coolant temperature, thereby ensuring that the engine can be started smoothly in a low-temperature environment. The use of a PID control algorithm can make the heating process of the coolant more stable and controllable, and avoid temperature changes that are too fast or too slow. Fuzzy logic control and machine learning algorithms can combine the ambient temperature, coolant temperature, and the state of the heating device to predict when the coolant has reached the temperature required for starting, thereby making an accurate start-up judgment. By optimizing the heating time and power control, the system not only improves the heating efficiency, but also avoids unnecessary energy waste. Uniform heating of the coolant can prevent overheating of certain components inside the engine, thereby preventing thermal damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a control method for auxiliary start-up heating of engine coolant; DETAILED DESCRIPTION

[0009] A control method for auxiliary start-up heating of engine coolant comprises the following steps; S1. Monitoring ambient temperature: The temperature sensor is activated. When the vehicle is stationary or just started, the ambient temperature sensor (usually installed on the outside of the vehicle) starts working. The system detects the external ambient temperature in real time. If the external temperature is detected to be lower than the set critical value (such as 0°C or -5°C), the low-temperature preheating mode is triggered. Wake-up preheating program: When the ambient temperature is lower than the set value, the preheating system is awakened and ready to heat the coolant; S2. Detect engine coolant temperature: Coolant temperature sensor monitoring: The coolant temperature sensor (usually installed in the engine block or coolant circuit) monitors the current coolant temperature, and the temperature threshold is judged: If the coolant temperature is lower than the minimum temperature required for normal engine operation (for example, lower than 20°C), the heating system enters the next step to prepare for starting, and compares the ambient and coolant temperatures: The system compares the ambient temperature and the coolant temperature to decide whether to continue the coolant heating or directly prepare to start the engine; S3. Start the coolant heating device: The system starts the coolant heating device, usually using a PTC (positive temperature coefficient) heater or a resistance heater, connected to the engine coolant circuit, heater parameter setting: The system calculates the required heating power and time based on the ambient temperature, coolant temperature and power supply status. At low temperatures: If the temperature is low, the system will start the heater with a higher power, usually working at full load. When the temperature approaches the target: As the coolant temperature approaches the predetermined heating target, the system automatically reduces the heater power to avoid overheating; S4. Heating time and power control: During the heating process, the coolant temperature sensor continuously monitors the temperature change of the coolant and uses the PID control algorithm to adjust the power of the heater in real time to make the coolant temperature change smooth and controllable. Combined with the machine learning algorithm, the temperature change pattern is analyzed based on historical data to further optimize the heating time and power control, and the preset target is that the coolant temperature reaches the range of 30°C-50°C; S5. Circulating coolant: During the heating process, the coolant circulation pump starts to work, circulating the heated coolant to various parts of the engine cooling system, ensuring that the temperature of important parts of the engine block and cylinder head gradually rises. The coolant enters the engine block through the pipeline from the heater and then returns to the heater for secondary heating, forming a closed-loop cycle. Through the circulation of the coolant, local overheating is prevented, and the overall temperature of the system is accelerated to rise, so that all parts of the engine are heated evenly; S6. Judgment before engine start: During the heating process, the coolant temperature is continuously detected, based on the current coolant temperature, external environment and the working status of the heating device, and the formula is integrated: , where Tc is the current coolant temperature (°C), Ta is the ambient temperature (°C), S is the working state of the heating device (0 means not working, 1 means working), k is the temperature rise rate coefficient (depends on the power and efficiency of the heating device, in °C / s), t: time (seconds), based on the formula, predict whether the coolant has reached the temperature required for starting, and make a start judgment in advance, and use the fuzzy logic judgment model to judge whether the start conditions are met. When the start conditions are met, the system sends a signal to allow the driver and the automatic control system to start the engine; S7. Stop heating device: After the engine is started, the coolant heater automatically stops working to avoid overheating. The coolant pump continues to work, and the engine maintains its operating temperature through the normal cooling circulation system. The heater enters standby mode and monitors the temperature at any time. When the system temperature drops to the set value again, the heating can be restarted; S8. Fault detection and safety protection: The system will detect the working status of key equipment such as the heater, power supply, coolant circulation pump, etc. to ensure the normal operation of the entire heating system. If the heater is overloaded, the temperature is too high, or the circulation is not smooth (such as water pump failure), the system will issue an alarm signal and automatically cut off the power supply to prevent further damage. After detecting a fault, the system will take different countermeasures according to the type of fault, such as reducing the heater power, restarting the circulation pump, or directly stopping heating.

[0010] Furthermore, a control method for auxiliary starting heating of engine coolant is provided. In step S4, the PID control algorithm is used to adjust the power of the heater in real time to make the change of the coolant temperature controllable. The machine learning algorithm is combined to analyze the temperature change pattern according to historical data to further optimize the heating time and power control. The specific steps are as follows; S41. Initialize the PID controller: the input is the current coolant temperature and the target temperature, the output is the heater power, set the target coolant temperature range, and input the current coolant temperature as feedback to the PID controller, and adjust the heater power according to the PID calculation result. The power adjustment range should be limited according to the specifications and design of the heater (for example, between 0-100%), while avoiding overheating; S42. Real-time feedback adjustment: monitor the coolant temperature in real time and continuously feed back the current temperature to the PID controller to ensure that the system continuously optimizes the heating process and avoids over-high or under-low temperatures; S43. Combine machine learning algorithms to optimize heating time and power control; S431. Data collection and preprocessing: Collect data from the historical heating process, including ambient temperature, initial coolant temperature, heating power, heating time, and target temperature, clean the data, process outliers and missing values, and standardize and normalize the data to ensure that the data can be effectively trained in the machine learning model; S432. Model training: Use historical data to train a decision tree regression model so that it can understand the temperature variation pattern and heater power adjustment mode during the heating process, select key features that affect the heating process: ambient temperature, current coolant temperature, heater power as model inputs, and optimize the model through cross-validation and hyperparameter adjustment methods; S433. Prediction and optimization: Based on the decision tree regression model, the heating process is predicted. According to the predicted heating process, accurate heating power adjustment suggestions are provided. The best match between temperature rise and power change in historical data is analyzed through machine learning algorithms to optimize the heating process, shorten the heating time, and improve energy efficiency. S434. Real-time feedback and dynamic adjustment: Feedback the output of the decision tree regression model to the PID control system to adjust the heating power in real time. The optimization results of the model provide more accurate adjustment parameters for the PID controller, making the heating process more efficient, and constantly learning and adapting to new environments and working conditions to achieve gradual optimization; S44. Closed-loop system combining PID control and machine learning: Based on the above steps, the PID control algorithm and the decision tree regression model are combined to form a closed-loop system that adjusts the heater power in real time and performs self-optimization based on historical data; S45. Self-learning and improvement: As the system continues to run, the decision tree regression model continuously learns from new heating data and optimizes the heating strategy so that the system can cope with various working conditions and environmental conditions. After the system learns the new mode, the parameters of the PID controller are adjusted according to the feedback from machine learning to improve heating efficiency and accuracy.

[0011] Furthermore, a control method for auxiliary starting heating of engine coolant is provided. In step S6, the temperature of the coolant is continuously detected during the heating process, and the formula is obtained based on the current coolant temperature, the external environment and the working state of the heating device: ,in, Indicates time t The coolant temperature at the moment, Tc is the current coolant temperature, Ta is the external environment temperature, S is the working state of the heating device, k is the temperature rise rate coefficient, t represents time, based on the formula to predict whether the coolant has reached the temperature required for startup, the specific steps are as follows; S61. Parameter definition: current coolant temperature Tc, obtained from the temperature sensor, input range 0°C to 100°C, external ambient temperature Ta, obtained through external temperature sensor, weather API, affecting the rate of increase of coolant temperature, heating device working state S, including heating power, working time, input range: 0 (stop), 1 (working), target temperature Tg, preset optimal coolant temperature required for engine start; S62. Fuzzy set definition: Coolant temperature Tc is defined as "very cold", "cold", "moderate", "warm", "very warm"; ambient temperature Ta is defined as "extremely cold", "cold", "cool", "warm", "hot"; heating device working state S is defined as "inefficient", "moderate", "efficient"; S63. Fuzzy rule making: Rule 1: If the coolant temperature is "very cold" and the ambient temperature is "extremely cold", the start condition is "not met"; Rule 2: If the coolant temperature is "cold" and the ambient temperature is "cold", the start condition "may not be satisfied"; Rule 3: If the coolant temperature is "moderate" and the ambient temperature is "cool", the start condition is "likely to be met"; Rule 4: If the coolant temperature is "warm" and the ambient temperature is "warm", then the start condition is "satisfied" Rule 5: If the coolant temperature is "very warm" and the ambient temperature is "hot", the start condition is "highly satisfied"; S64. Fuzzification of input variables: converting the actually measured coolant temperature, external environment temperature and heating device working state into corresponding fuzzy set membership degrees; S65. Rule evaluation: Evaluate each rule and calculate the fuzzy set membership of the output result of each rule; S66. Output variable defuzzification: Use the weighted average method to combine the output results of multiple rules and integrate them to obtain the formula: , make the final start condition judgment according to the formula; S67. Startup judgment: If the startup condition is "not satisfied" or "may not be satisfied": continue heating and re-evaluate regularly. If the startup condition is "may be satisfied": prompt the user to choose to start and wait for a while before starting. If the startup condition is "satisfied" or "highly satisfied": allow startup and record the relevant data of this startup; S68. Feedback and learning: Get new input data in real time: coolant temperature, ambient temperature, heater status, and continuously adjust the startup judgment according to the above steps. Every time the sensor reads new data, the system needs to re-fuzzify, reason, defuzzify and judge to make a startup decision.

[0012] A control system for auxiliary start-up heating of an engine coolant, the control system for auxiliary start-up heating of an engine coolant is used to implement a control method for auxiliary start-up heating of an engine coolant; the control system for auxiliary start-up heating of an engine coolant comprises: a data acquisition and sensor monitoring module, a fuzzy logic reasoning and PID control module, a heating process optimization and control module, a judgment and decision module, and a safety and fault protection module; The data acquisition and sensor monitoring module collects the ambient temperature, coolant temperature and heating device status in real time through sensors, and sends the data to the fuzzy logic reasoning module and PID control module; Fuzzy logic reasoning and PID control module: The fuzzy logic module determines whether the system can be started based on the ambient temperature, coolant temperature and heater status. The PID controller adjusts the power of the heater in real time to ensure that the coolant temperature rises gradually. Heating process optimization and control module: optimizes heating time and power regulation based on decision tree regression model to ensure efficient and smooth heating process; Judgment and decision module: The system determines whether the starting conditions are met based on fuzzy logic. When the coolant has reached the starting temperature, the engine is started; if not, heating continues; Safety and fault protection module: The system monitors the status of heaters and power supply equipment in real time to ensure safe operation of the system. When a fault occurs, a warning is issued and protective measures are taken.

Claims

1. A control method for auxiliary starting heating of engine coolant, characterized in that: The steps include: S1. Monitor ambient temperature: The temperature sensor is activated. When the vehicle is stationary or just started, the ambient temperature sensor starts working. The system detects the external ambient temperature in real time. When the external temperature is detected to be lower than the set critical value: -5°C, the low-temperature preheating mode is triggered and the preheating program is awakened: when the ambient temperature is lower than the set value, the preheating system is awakened and ready to heat the coolant; S2. Detecting the engine coolant temperature: Use the coolant temperature sensor to monitor the current coolant temperature and determine the temperature threshold. If the coolant temperature is lower than the minimum temperature required for the normal operation of the engine, the heating system will enter the next step to prepare for starting. Compare the ambient and coolant temperatures to decide whether to continue cooling the coolant or directly prepare to start the engine. S3. Start the coolant heating device: The system starts the coolant heating device PTC, which is connected to the engine coolant circuit. The system calculates the required heating power and time according to the ambient temperature, coolant temperature and power supply status. When the temperature is low, the system starts the heater at high power and works at full load. As the coolant temperature approaches the predetermined heating target, the system automatically reduces the heater power. S4. Heating time and power control: During the heating process, the coolant temperature sensor continuously monitors the temperature change of the coolant, and uses the PID control algorithm to adjust the power of the heater in real time to make the coolant temperature change smooth. Combined with the machine learning algorithm, the temperature change pattern is analyzed based on historical data to optimize the heating time and power control, and the preset target is that the coolant temperature reaches the range of 30°C-50°C; S5. Circulating coolant: During the heating process, the coolant circulation pump starts to work, circulating the heated coolant to various parts of the engine cooling system. The temperature of the important parts of the engine block and cylinder head gradually rises. The coolant enters the engine block through the pipeline from the heater and then returns to the heater for secondary heating, forming a closed-loop circulation. The coolant circulation prevents local overheating and accelerates the temperature rise of the entire system, so that all parts of the engine are evenly heated. S6. Judgment before engine start: During the heating process, the coolant temperature is continuously detected, based on the current coolant temperature, external environment and the working status of the heating device, and the formula is integrated: ,in Indicates time t The coolant temperature at the moment, Tc is the current coolant temperature, Ta is the external environment temperature, S is the working state of the heating device, k is the temperature rise rate coefficient, t represents time, based on the formula, it is predicted whether the coolant has reached the temperature required for starting, and a start judgment is made in advance. The fuzzy logic judgment model is used to determine whether the start conditions are met. When the start conditions are met, the system sends a signal to allow the driver to control the system to start the engine; S7. Stop heating device: After the engine is started, the coolant heater automatically stops working to avoid overheating. The coolant pump continues to work, and the engine maintains its operating temperature through the normal cooling circulation system. The heater enters standby mode and monitors the temperature at any time. When the system temperature drops to the set value again, the heating is restarted; S8. Fault detection and safety protection: The system detects the working status of the heater, power supply, and coolant circulation pump equipment. When the heater is overloaded, the temperature is too high, or the circulation is not smooth, the system will send out an alarm signal and automatically cut off the power supply to prevent further damage. After detecting a fault, the system will take different countermeasures according to the type of fault: reduce the heater power, restart the circulation pump, or directly stop heating.

2. The control method for auxiliary starting heating of engine coolant according to claim 1, characterized in that: In step S4, the PID control algorithm is used to adjust the power of the heater in real time to make the change of the coolant temperature controllable. The machine learning algorithm is combined to analyze the temperature change pattern according to historical data to further optimize the heating time and power control. The specific steps are as follows; S41. Initialize the PID controller: the input is the current coolant temperature and the target temperature, the output is the heater power, set the target coolant temperature range, and input the current coolant temperature as feedback to the PID controller, and adjust the heater power according to the PID calculation results. The power adjustment range should be limited according to the specifications and design of the heater, while avoiding overheating; S42. Real-time feedback adjustment: monitor the coolant temperature in real time and continuously feed back the current temperature to the PID controller; S43. Combine machine learning algorithms to optimize heating time and power control; S431. Data collection and preprocessing: Collect data from the historical heating process, including ambient temperature, initial coolant temperature, heating power, heating time, target temperature, and clean the data, process outliers and missing values, and standardize and normalize the data; S432. Model training: Use historical data to train a decision tree regression model, select key features that affect the heating process: ambient temperature, current coolant temperature, heater power as model inputs, and optimize the model through cross-validation and hyperparameter adjustment methods; S433. Prediction and optimization: Based on the decision tree regression model, the heating process is predicted. According to the predicted heating process, accurate heating power adjustment suggestions are provided. The best match between temperature rise and power change in historical data is analyzed to optimize the heating process, shorten the heating time and improve energy efficiency. S434. Real-time feedback and dynamic adjustment: Feedback the output of the decision tree regression model to the PID control system to adjust the heating power in real time. The optimization results of the model provide accurate parameter adjustment for the PID controller, which continuously learns and adapts to new environments and working conditions to achieve gradual optimization. S44. Closed-loop system combining PID control and machine learning: Based on the above steps, the PID control algorithm and the decision tree regression model are combined to form a closed-loop system that adjusts the heater power in real time and performs self-optimization based on historical data; S45. Self-learning and improvement: As the system continues to run, the decision tree regression model continuously learns from new heating data and optimizes the heating strategy so that the system can cope with various working conditions and environmental conditions. After the system learns the new mode, the parameters of the PID controller are adjusted according to the feedback from machine learning to improve heating efficiency and accuracy.

3. The control method for auxiliary starting heating of engine coolant according to claim 1, characterized in that: In step S6, the temperature of the coolant is continuously detected during the heating process, and the formula is obtained based on the current coolant temperature, the external environment and the working state of the heating device: ,in, Indicates time t The coolant temperature at the moment, Tc is the current coolant temperature, Ta is the external environment temperature, S is the working state of the heating device, k is the temperature rise rate coefficient, t represents time, based on the formula to predict whether the coolant has reached the temperature required for startup, the specific steps are as follows; S61. Parameter definition: current coolant temperature Tc, obtained from the temperature sensor, input range 0°C to 100°C, external ambient temperature Ta, obtained through external temperature sensor, weather API, affecting the rate of increase of coolant temperature, heating device working state S, including heating power, input range: 0 stop, 1 working, target temperature Tg, preset optimal coolant temperature required for engine start; S62. Fuzzy set definition: Coolant temperature Tc is defined as "very cold", "cold", "moderate", "warm", "very warm"; ambient temperature Ta is defined as "extremely cold", "cold", "cool", "warm", "hot"; heating device working state S is defined as "inefficient", "moderate", "efficient"; S63. Fuzzy rule making: Rule 1: When the coolant temperature is "very cold" and the ambient temperature is "extremely cold", the start condition is "not met"; Rule 2: When the coolant temperature is "cold" and the ambient temperature is "cold", the start condition "may not be satisfied"; Rule 3: When the coolant temperature is "moderate" and the ambient temperature is "cool", the start condition is "likely to be met"; Rule 4: When the coolant temperature is "warm" and the ambient temperature is "warm", the start condition is "satisfied"; Rule 5: When the coolant temperature is "very warm" and the ambient temperature is "hot", the start condition is "highly satisfied"; S64. Fuzzification of input variables: converting the actually measured coolant temperature, external environment temperature and heating device working state into corresponding fuzzy set membership degrees; S65. Rule evaluation: Evaluate each rule and calculate the fuzzy set membership of the output result of each rule; S66. Output variable defuzzification: Use the weighted average method to combine the output results of multiple rules and integrate them to obtain the formula: , make the final start condition judgment according to the formula; S67. Startup judgment: When the startup condition is "not satisfied" or "may not satisfied", heating will continue and regular re-evaluation will be conducted. When the startup condition is "may be satisfied", the user will be prompted to select startup and wait for a while before starting. When the startup condition is "satisfied" or "highly satisfied", startup will be allowed and relevant data of the startup will be recorded. S68. Feedback and learning: Obtain new input data in real time: coolant temperature, ambient temperature, heater status, and continuously adjust the startup judgment according to the above steps. Each time the sensor reads new data, the system needs to re-fuzzify, reason, defuzzify and judge to make a startup decision.

4. A control system for auxiliary start-up heating of engine coolant, characterized in that: The control system of the engine coolant auxiliary start heating is used to implement the control method of the engine coolant auxiliary start heating as claimed in any one of claims 1 to 3; the control system of the engine coolant auxiliary start heating comprises: a data acquisition and sensor monitoring module, a fuzzy logic reasoning and PID control module, a heating process optimization and control module, a judgment and decision module, and a safety and fault protection module; The data acquisition and sensor monitoring module collects the ambient temperature, coolant temperature and heating device status in real time through sensors, and sends the data to the fuzzy logic reasoning module and PID control module; Fuzzy logic reasoning and PID control module: The fuzzy logic module determines whether the system can be started based on the ambient temperature, coolant temperature and heater status. The PID controller adjusts the power of the heater in real time to ensure that the coolant temperature rises gradually. Heating process optimization and control module: optimizes heating time and power regulation based on decision tree regression model to ensure efficient and smooth heating process; Judgment and decision module: The system determines whether the starting conditions are met based on fuzzy logic. When the coolant has reached the starting temperature, the engine is started; if not, heating continues; Safety and fault protection module: The system monitors the status of heaters and power supply equipment in real time to ensure safe operation of the system. When a fault occurs, a warning is issued and protective measures are taken.

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