A control method and system for engine coolant assisted start heating

Abstract

The application discloses an engine coolant auxiliary starting heating control method and system, aiming to solve the problems of difficult cold start, uneven heating and low energy efficiency of the engine in cold environments. By monitoring the ambient temperature and coolant temperature in real time, the system intelligently determines when to start the heating device and automatically adjusts the heating power and time according to environmental changes. The PID control algorithm is used to regulate the heater power, ensuring a smooth and controllable temperature rise process and avoiding excessive or slow temperature rise. The coolant circulating pump ensures uniform distribution of the heated coolant inside the engine, preventing local overheating. Combined with fuzzy logic reasoning and machine learning algorithms, the system optimizes the heating process based on historical data, further improving energy efficiency and heating accuracy. The system not only improves the starting performance of the engine under low temperature conditions, but also optimizes energy utilization during the heating process, with high intelligence and safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engine start control, specifically to a control method and system for engine coolant auxiliary start heating. BACKGROUND

[0002] In cold environments, the engine often faces the problem of low coolant temperature during cold start, which leads to the engine failing to start smoothly. At low temperatures, the engine oil inside the engine becomes thick, and the coolant also becomes viscous, increasing the difficulty of starting. This difficulty in starting in low-temperature environments not only increases fuel consumption but also can damage the internal components of the engine, shortening the service life of the engine. Traditional cold start heating methods may have the problem of uneven heating of the coolant, leading to overheating or insufficient heating of certain engine parts. Uneven heating of the coolant can cause local overheating of some engine parts (such as cylinders, cylinder heads, etc.), while other parts are still in a low-temperature state, which can cause thermal damage, deformation, or imbalance, affecting the normal operation of the engine. Traditional heating devices may not accurately control the heating power during temperature rise, leading to 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 damaging the system; while slow heating may cause the engine to fail to reach the working temperature in time, affecting the starting efficiency and the operation of the vehicle. SUMMARY

[0003] A control method for engine coolant auxiliary start heating, comprising the following steps:

[0004] S1. Monitor the ambient temperature: The temperature sensor is started, and when the vehicle is stationary or just started, the ambient temperature sensor (usually installed outside the vehicle) starts working, and the system detects the external environment temperature in real time. If the outside temperature is detected to be lower than the set critical value (for example, 0°C or -5°C), the low-temperature preheating mode is triggered. Wake up the preheating program: After the ambient temperature is lower than the set value, the preheating system is woken up and prepares to heat the coolant;

[0005] S2. Detect the 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, temperature threshold judgment: 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 of preparing to start, and compares the ambient and coolant temperatures: The system compares the ambient and coolant temperatures to decide whether to continue heating the coolant or directly prepare to start the engine;

[0006] S3. Start the cooling liquid heating device: the system starts the cooling liquid heating device, usually using PTC (positive temperature coefficient) heater or resistance heater, connected to the engine cooling liquid circuit, heater parameter setting: the system calculates the required heating power and time according to the ambient temperature, cooling liquid temperature and power supply state, low temperature: if the temperature is low, the system will start the heater with high power, usually full load, temperature close to target: as the cooling liquid temperature approaches the predetermined heating target, the system automatically reduces the power of the heater to avoid overheating;

[0007] S4. Heating time and power control: during the heating process, the cooling liquid temperature sensor continuously monitors the temperature change of the cooling liquid, and uses the PID control algorithm to adjust the power of the heater in real time, so that the temperature change of the cooling liquid is smooth and controllable, combined with machine learning algorithm, according to the historical data analysis temperature change mode, further optimize the heating time and power control, and preset the target as the cooling liquid temperature reaches 30°C-50°C range;

[0008] S5. Circulating cooling liquid: during the heating process, the cooling liquid circulating pump starts to work, circulating the heated cooling liquid to each part of the engine cooling system, ensuring the temperature of the engine cylinder, cylinder head and other important parts gradually rises, the cooling liquid from the heater enters the engine cylinder through the pipeline, and then returns to the heater for secondary heating, forming a closed loop circulation. Through the circulation of cooling liquid, local overheating is prevented, and the overall temperature rise of the system is accelerated, so that each part of the engine is uniformly heated;

[0009] S6. Engine start before judgment: during the heating process, the temperature of the cooling liquid is continuously detected, based on the current cooling liquid temperature, external environment and working state of the heating device, and the formula is obtained by integration: , where Tc is the current cooling liquid temperature (°C), Ta is the external environment 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, unit: °C / s), t: time (second), based on the formula to predict whether the cooling liquid has reached the required temperature for starting, and make a start judgment in advance. When the start condition is met, the system sends a signal to allow the driver, automatic control system to start the engine;

[0010] S7. Stop the heating device: after the engine starts, the cooling liquid heater automatically stops working to avoid overheating, the cooling liquid pump continues to work, and the engine maintains its working temperature through the normal cooling circulation system. The heater enters standby state and monitors the temperature at any time. When the system temperature decreases to the set value again, the heating can be restarted;

[0011] S8. Fault detection and safety protection: The system will detect the working status of key devices such as heaters, power supplies, cooling liquid circulation pumps, 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 (for example, water pump failure), the system will issue an alarm signal and automatically cut off the power supply to prevent further damage. After detecting the fault, the system will take different measures according to the fault type, such as reducing the heater power, restarting the circulation pump or directly stopping heating.

[0012] Further, a control method for engine coolant auxiliary start-up heating,

[0013] In step S4, the PID control algorithm is used to adjust the power of the heater in real time, so that the temperature change of the coolant is controllable. Combined with the machine learning algorithm, the temperature change pattern is analyzed according to the historical data to further optimize the heating time and power control. The specific steps are as follows:

[0014] S41. Initialize the PID controller: input the current coolant temperature, target temperature, output the heater power, set the target coolant temperature range, and input the current coolant temperature to the PID controller as feedback, and adjust the power of the heater according to the PID calculation result. The adjustment range of the power should be limited according to the specifications and design of the heater (for example, between 0-100%), while avoiding excessive heating;

[0015] S42. Real-time feedback adjustment: Real-time monitoring of coolant temperature and continuous feedback of current temperature to PID controller to ensure continuous optimization of heating process and avoid excessive or insufficient temperature;

[0016] S43. Optimize heating time and power control combined with machine learning algorithm;

[0017] S431. Data collection and preprocessing: Collect data during the historical heating process, including environmental temperature, initial coolant temperature, heating power, heating time, target temperature, and clean the data, process outliers, missing values, standardize and normalize the data to ensure effective training of the data in the machine learning model;

[0018] S432. Model training: Use historical data to train the decision tree regression model to understand the temperature change pattern and heater power adjustment mode during the heating process. Select the key features that have an impact on the heating process: environmental temperature, current coolant temperature, heater power as model input, and optimize the model through cross-validation and hyperparameter adjustment method;

[0019] S433. Prediction and Optimization: Based on the decision tree regression model, the heating process is predicted, and according to the predicted heating process, accurate heating power adjustment suggestions are provided, and through machine learning algorithm analysis of historical data, the best matching of temperature rise and power change is analyzed, the heating process is optimized, the heating time is shortened, and the energy efficiency is improved;

[0020] S434. Real-time Feedback and Dynamic Adjustment: The output of the decision tree regression model is fed back to the PID control system, and the heating power is adjusted 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 environmental and working conditions to achieve gradual optimization;

[0021] 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 optimizes itself based on historical data;

[0022] 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, enabling the system to cope with various working conditions and environmental conditions. After the system learns new patterns, the parameters of the PID controller are adjusted based on the feedback of machine learning to improve heating efficiency and accuracy.

[0023] Further, a control method for auxiliary starting heating of engine coolant,

[0024] In step S6, the temperature of the coolant is detected during the heating process, based on the current coolant temperature, the external environment and the working state of the heating device, and the formula is obtained by integration: wherein, represents the temperature of the coolant at time t, 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 required temperature for starting, the specific steps are as follows: t

[0025] S61. Parameter definition: current coolant temperature Tc, obtained from temperature sensor, input range 0°C to 100°C, external environment temperature Ta, obtained through external temperature sensor, weather API, heating rate affecting 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 starting;

[0026] ​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 "low efficiency", "medium", "high efficiency";

[0027] S63. Fuzzy rule making:

[0028] Rule 1: If coolant temperature is "very cold" and ambient temperature is "extremely cold", then the start condition is "not satisfied";

[0029] Rule 2: If coolant temperature is "cold" and ambient temperature is "cold", then the start condition is "possibly not satisfied";

[0030] Rule 3: If coolant temperature is "moderate" and ambient temperature is "cool", then the start condition is "possibly satisfied";

[0031] Rule 4: If coolant temperature is "warm" and ambient temperature is "warm", then the start condition is "satisfied"

[0032] Rule 5: If coolant temperature is "very warm" and ambient temperature is "hot", then the start condition is "highly satisfied";

[0033] S64. Fuzzification of input variables: convert the actual measured coolant temperature, ambient temperature and heating device working state into corresponding fuzzy set membership degrees;

[0034] S65. Rule evaluation: evaluate each rule and calculate the fuzzy set membership degree of the output result of each rule;

[0035] S66. Defuzzification of output variables: use weighted average method to combine the output results of multiple rules, and integrate the formula: , according to the formula to make the final start condition judgment;

[0036] S67. Start judgment: start condition "not satisfied", "possibly not satisfied": continue heating and re-evaluate regularly, when start condition "possibly satisfied": prompt user to choose start, wait for a period of time and then start start condition "satisfied", "highly satisfied": allow start, and record the relevant data of this start;

[0037] S68. Feedback and learning: real-time acquisition of new input data: coolant temperature, ambient temperature, heater state, and continuous adjustment of start judgment according to the above steps. Each time the sensor reads new data, the system needs to re-fuzzify, reason, de-fuzzify and judge, so as to make start decision.

[0038] The application discloses an engine coolant auxiliary starting heating control system for realizing an engine coolant auxiliary starting heating control method.

[0039] The data acquisition and sensor monitoring module acquires the ambient temperature, coolant temperature and heating device state in real time through sensors, and sends the data to the fuzzy logic reasoning module and the PID control module.

[0040] The fuzzy logic reasoning and PID control module judges whether starting is possible according to the ambient temperature, coolant temperature and heater state, and the PID controller adjusts the power of the heater in real time to ensure that the coolant temperature gradually increases.

[0041] The heating process optimization and control module optimizes the heating time and power adjustment based on a decision tree regression model to ensure that the heating process is efficient and smooth.

[0042] The judgment and decision module judges whether the starting condition is met according to the fuzzy logic, and starts the engine when the coolant has reached the starting temperature, and continues to heat when the condition is not met.

[0043] The safety and fault protection module monitors the state of the heater and power supply equipment in real time to ensure the safe operation of the system, and issues a warning and takes protective measures when a fault occurs.

[0044] The application has the beneficial effects that whether the heater needs to be started can be judged according to the ambient temperature and coolant temperature, and the engine can be started smoothly in a low-temperature environment. The use of the PID control algorithm can make the heating process of the coolant more stable and controllable, and avoid too fast or too slow temperature change. The fuzzy logic control and machine learning algorithm can comprehensively consider the ambient temperature, coolant temperature and heating device state to predict when the coolant has reached the required starting temperature, so as to make an accurate starting judgment. The system optimizes the heating time and power control, which not only improves the heating efficiency, but also avoids unnecessary energy waste. Uniform heating of the coolant can avoid overheating of some parts in the engine, thereby preventing thermal damage. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 It is a flowchart of an engine coolant auxiliary starting heating control method. DETAILED DESCRIPTION

[0046] The application discloses an engine coolant auxiliary starting heating control method, which comprises the following steps.

[0047] S1. Monitor ambient temperature: When the vehicle is stationary or just started, the ambient temperature sensor (usually installed outside the vehicle) starts working, the system detects the external environment temperature in real time, if the detected external temperature is lower than the set threshold value (for example 0°C or -5°C), the low-temperature preheating mode is triggered. Wake up the preheating program: After the ambient temperature is lower than the set value, the preheating system is woken up and prepares to heat the coolant;

[0048] 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, temperature threshold judgment: 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 to start, Compare ambient and coolant temperature: The system compares the ambient temperature and the coolant temperature to determine whether to continue heating the coolant or directly prepare to start the engine;

[0049] S3. Start coolant heating device: The system starts the coolant heating device, usually using PTC (Positive Temperature Coefficient) heater or resistance heater, connected to the engine coolant circuit, heater parameter setting: The system calculates the required heating power and time according to the ambient temperature, coolant temperature and power supply state, Low temperature: If the temperature is low, the system will start the heater at a higher power, usually full load, Temperature close to target: As the coolant temperature approaches the predetermined heating target, the system automatically reduces the power of the heater to avoid overheating;

[0050] 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, so that the temperature change of the coolant is smooth and controllable. Combined with machine learning algorithm, according to historical data analysis temperature change mode, further optimize heating time and power control, and preset target for coolant temperature to reach 30°C-50°C range;

[0051] S5. Circulating coolant: During the heating process, the coolant circulating pump starts working, circulating the heated coolant to various parts of the engine cooling system, ensuring that the temperature of the engine block, cylinder head and other important parts gradually rises. The coolant from the heater enters the engine block through the pipeline and returns to the heater for secondary heating, forming a closed loop circulation. Through the circulation of the coolant, local overheating is prevented, and the overall temperature rise of the system is accelerated, so that each part of the engine is uniformly heated;

[0052] S6. Engine start-up judgment: During the heating process, the temperature of the coolant is continuously detected, based on the current coolant temperature, external environment and working state of the heating device, and the formula is obtained: Where Tc is the current coolant temperature (°C), Ta is the ambient temperature (°C), S is the heating device working state (0 means not working, 1 means working), k is the temperature rise rate coefficient (depends on the heating device power and efficiency, unit is °C / s), t: time (seconds), based on the formula to predict whether the coolant has reached the required temperature for starting, and make a start-up judgment in advance, through the fuzzy logic judgment model to determine whether the starting conditions are met, when the starting conditions are met, the system sends a signal to allow the driver, automatic control system to start the engine;

[0053] S7. Stop the heating device: after the engine starts, the coolant heater automatically stops working to avoid overheating, the coolant pump continues to work, and the engine maintains its working temperature through the normal cooling circulation system. The heater enters standby state and monitors the temperature at any time. When the system temperature decreases to the set value again, the heating can be restarted;

[0054] S8. Fault detection and safety protection: the system will detect the working state of the heater, power supply, coolant circulating pump and other key equipment 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 (for example, water pump failure), the system will send an alarm signal and automatically cut off the power supply to prevent further damage. After detecting the fault, the system takes different measures according to the fault type, such as reducing the heater power, restarting the circulating pump or directly stopping heating.

[0055] Further, a control method for engine coolant auxiliary start-up heating,

[0056] In step S4, the PID control algorithm is used to adjust the power of the heater in real time, so that the temperature change of the coolant is controllable. Combined with machine learning algorithm, the temperature change pattern is analyzed according to historical data to further optimize the heating time and power control. The specific steps are as follows:

[0057] S41. Initialize PID controller: input the current coolant temperature and target temperature, output the heater power, set the target coolant temperature range, and input the current coolant temperature to the PID controller as feedback. According to the PID calculation result, adjust the power of the heater. The adjustment range of power should be limited according to the specifications and design of the heater (for example, between 0-100%), while avoiding excessive heating;

[0058] S42. Real-time feedback adjustment: real-time monitoring of coolant temperature and continuous feedback of current temperature to PID controller to ensure continuous optimization of heating process and avoid excessive or insufficient temperature;

[0059] S43. Combine machine learning algorithm to optimize heating time and power control;

[0060] S431. Data collection and preprocessing: Collect data during historical heating processes, including ambient temperature, coolant initial temperature, heating power, heating time, target temperature, and clean the data, handle outliers, missing values, standardize and normalize the data to ensure effective training in machine learning models;

[0061] S432. Model training: Use historical data to train a decision tree regression model to understand the temperature change 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 input, optimize the model through cross-validation and hyperparameter adjustment method;

[0062] S433. Prediction and optimization: Based on the decision tree regression model, predict the heating process, provide accurate heating power adjustment suggestions according to the predicted heating process, and analyze the best match between temperature rise and power change in historical data through machine learning algorithm to optimize the heating process, shorten the heating time and improve energy efficiency;

[0063] 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 to make the heating process more efficient, and continuously learn and adapt to new environmental and working conditions to achieve gradual optimization;

[0064] 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 optimizes itself based on historical data;

[0065] 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, enabling the system to cope with various working conditions and environmental conditions. After the system learns new patterns, the parameters of the PID controller are adjusted based on the feedback of machine learning to improve heating efficiency and accuracy.

[0066] Further, a control method for auxiliary starting heating of engine coolant,

[0067] In step S6, the temperature of the coolant is detected during the heating process, based on the current coolant temperature, the external environment and the working state of the heating device, and the formula is obtained by integration: where, t represents time tThe current coolant temperature Tc is obtained from the temperature sensor, the input range is 0°C to 100°C, the ambient temperature Ta is obtained by an external temperature sensor, weather API, and influences the rate of increase of the coolant temperature, the heating device working state S includes heating power, working time, and the input range is: 0 (stop), 1 (working), and the target temperature Tg is the preset optimal coolant temperature required for engine start;

[0068] S61. Parameter definition: the current coolant temperature Tc is obtained from the temperature sensor, the input range is 0°C to 100°C, the ambient temperature Ta is obtained by an external temperature sensor, weather API, and influences the rate of increase of the coolant temperature, the heating device working state S includes heating power, working time, and the input range is: 0 (stop), 1 (working), and the target temperature Tg is the preset optimal coolant temperature required for engine start;

[0069] S62. Fuzzy set definition: the coolant temperature Tc is defined as "very cold", "cold", "moderate", "warm", and "very warm"; the ambient temperature Ta is defined as "extremely cold", "cold", "cool", "warm", and "hot"; and the heating device working state S is defined as "inefficient", "moderate", and "efficient";

[0070] S63. Fuzzy rule making:

[0071] Rule 1: if the coolant temperature is "very cold" and the ambient temperature is "extremely cold", the start condition is "not met";

[0072] Rule 2: if the coolant temperature is "cold" and the ambient temperature is "cold", the start condition is "possibly not met";

[0073] Rule 3: if the coolant temperature is "moderate" and the ambient temperature is "cool", the start condition is "possibly met";

[0074] Rule 4: if the coolant temperature is "warm" and the ambient temperature is "warm", the start condition is "met"

[0075] Rule 5: if the coolant temperature is "very warm" and the ambient temperature is "hot", the start condition is "highly met";

[0076] S64. Fuzzy input variable: convert the actual measured coolant temperature, ambient temperature, and heating device working state into the corresponding fuzzy set membership degree;

[0077] S65. Rule evaluation: evaluate each rule and calculate the fuzzy set membership degree of the output result of each rule;

[0078] S66. Output variable defuzzification: use the weighted average method to combine the output results of multiple rules and integrate to obtain the formula: , the final start condition is judged according to the formula;

[0079] S67. Start judgment: start condition "not met", "may not be met": continue heating and reevaluate periodically, when start condition "may be met": prompt user to select start, wait for a period of time before starting start condition "met", "highly met": allow start, and record relevant data of this start;

[0080] S68. Feedback and learning: real-time acquisition of new input data: coolant temperature, ambient temperature, heater status, and continuous adjustment of start judgment according to the above steps. Each time the sensor reads new data, the system needs to re-fuzz, reason, de-fuzz, and judge to make a start decision.

[0081] An engine coolant auxiliary start heating control system for realizing an engine coolant auxiliary start heating control method; the engine coolant auxiliary start heating control system 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, a safety and fault protection module;

[0082] The data acquisition and sensor monitoring module: real-time acquisition of ambient temperature, coolant temperature and heating device status through sensors, data sent to fuzzy logic reasoning module and PID control module;

[0083] Fuzzy logic reasoning and PID control module: fuzzy logic module makes a judgment on whether it can start according to ambient temperature, coolant temperature and heater status, and PID controller adjusts the power of the heater in real time to ensure that the coolant temperature gradually rises;

[0084] Heating process optimization and control module: based on decision tree regression model to optimize heating time and power adjustment, ensure efficient and smooth heating process;

[0085] Judgment and decision module: the system judges whether the start condition is met according to fuzzy logic, starts the engine when the coolant has reached the start temperature; continue heating when it is not reached;

[0086] Safety and fault protection module: the system monitors the status of the heater and power supply equipment in real time to ensure safe operation of the system, and takes protective measures when a fault occurs.

Claims

1. A control method for engine coolant-assisted start-up heating, characterized by, Comprising the following steps: S1. Monitor ambient temperature: Temperature sensor starts, vehicle is stationary, just started, ambient temperature sensor starts working, the system detects the external environment temperature in real time, when detecting that the outside temperature is lower than the set critical value: -5°C, trigger low temperature preheating mode, wake up the preheating program: after the ambient temperature is lower than the set value, the preheating system is woken up, ready to cool the liquid heating; S2. Detect engine coolant temperature: Use coolant temperature sensor to monitor the current coolant temperature, temperature threshold judgment: the coolant temperature is lower than the minimum temperature required for normal engine operation, then the heating system enters the next step to prepare to start, compare the ambient and coolant temperature, decide whether to continue to heat the coolant or directly prepare to start the engine; S3. Start the coolant heating device: The system starts the coolant heating device PTC, 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 state, when the temperature is low, the system starts the heater with high power, full load work, as the coolant temperature approaches the predetermined heating target, the system automatically reduces the power of the heater; S4. Heating time and power control: During heating, the coolant temperature sensor continuously monitors the temperature change of the coolant, uses PID control algorithm to adjust the power of the heater in real time, makes the coolant temperature change smooth, combines machine learning algorithm, optimizes heating time and power control according to historical data analysis temperature change mode, and presets the target as the coolant temperature reaches 30°C-50°C range; S5. Circulating coolant: During heating, the coolant circulating pump starts to work, circulating the heated coolant to each part of the engine cooling system, the temperature of important parts such as engine block and cylinder head gradually rises, the coolant enters the engine block from the heater through the pipeline, and then returns to the heater for secondary heating, forming a closed loop circulation, through the circulation of coolant, preventing local overheating, at the same time, accelerating the overall temperature rise of the system, making the engine components evenly heated; S6. Judgment before engine start: Continuously detect the temperature of the coolant during the heating process, based on the current coolant temperature, the external environment and the working state of the heating device, and integrate the formula: wherein represents the coolant temperature at time t , Tc is the current coolant temperature, Ta is the ambient 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 required temperature for starting, and make a start-up judgment in advance, through a fuzzy logic judgment model to determine whether the start-up condition is met, when the start-up condition is 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 starts, the coolant heater automatically stops working to avoid over-heating, the coolant pump continues to work, the engine maintains its working temperature through the normal cooling circulation system, the heater enters standby state, and restarts heating when the system temperature decreases to the set value again; S8. Fault detection and safety protection: The system detects the working state of the heater, power supply and coolant circulating pump equipment, when the heater is overloaded, the temperature is too high, and the circulation is not smooth, the system sends an alarm signal and automatically cuts off the power supply to prevent further damage, after detecting the fault, the system takes different measures according to the fault type: reduce the power of the heater, restart the circulating pump, stop heating directly.

2. A control method for an engine coolant-assisted start-up heating as defined in claim 1, characterized in that, In step S4, PID control algorithm is used to adjust the power of the heater in real time, making the coolant temperature change controllable, combining machine learning algorithm, further optimizing heating time and power control according to historical data analysis temperature change mode, specific steps are as follows: S41. Initialize the PID controller: input the current coolant temperature, target temperature, output 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, while avoiding excessive heating; S42. Real-time feedback adjustment: real-time monitoring of coolant temperature and continuous feedback of current temperature to PID controller; S43. Combine machine learning algorithm to optimize heating time and power control; S431. Data collection and preprocessing: collect historical data during heating process, including ambient temperature, initial coolant temperature, heating power, heating time, target temperature, and clean data, process outliers, missing values, standardize and normalize data; S432. Model training: use historical data to train decision tree regression model, select key features that affect heating process: ambient temperature, current coolant temperature, heater power as model input, optimize model through cross-validation and hyperparameter adjustment method; S433. Prediction and optimization: based on decision tree regression model, predict the heating process, provide accurate heating power adjustment suggestions according to the predicted heating process, analyze the best match between temperature rise and power change in historical data, optimize the heating process, shorten the heating time, and improve energy efficiency; S434. Real-time feedback and dynamic adjustment: feedback the output of decision tree regression model to PID control system, real-time adjust heating power, the optimization result of model provides accurate parameter adjustment for PID controller, continuously learns and adapts to new environment and working conditions to achieve gradual optimization; S44. Closed-loop system combining PID control and machine learning: based on the above steps, PID control algorithm and decision tree regression model are combined to form a closed-loop system, which adjusts the heater power in real time and optimizes itself according to historical data; S45. Self-learning and improvement: as the system runs continuously, the decision tree regression model 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 new patterns, the parameters of PID controller are adjusted according to the feedback of machine learning to improve heating efficiency and accuracy.

3. The control method for engine coolant auxiliary start-up heating according to claim 1, characterized in that, The temperature of the cooling liquid is continuously detected during the heating process in step S6, and the formula is obtained based on the current cooling liquid temperature, the external environment and the working state of the heating device: wherein, represents the temperature of the cooling liquid at time t, Tc is the current cooling liquid temperature, Ta is the external environment temperature, S is the working state of the heating device, k is the temperature rise rate coefficient, and t represents time. t The formula is used to predict whether the cooling liquid has reached the required temperature for starting, and the specific steps are as follows: S61. Parameter definition: current coolant temperature Tc, obtained from temperature sensor, input range 0°C to 100°C, ambient temperature Ta, obtained through external temperature sensor, weather API, heating rate affecting 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-up; 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 state S is defined as "low efficiency", "medium efficiency", "high efficiency"; S63. Fuzzy rule making: Rule 1: when coolant temperature is "very cold" and ambient temperature is "extremely cold", the starting condition is "not met"; Rule 2: when coolant temperature is "cold" and ambient temperature is "cold", the starting condition is "possibly not met"; Rule 3: when coolant temperature is "moderate" and ambient temperature is "cool", the starting condition is "possibly met"; Rule 4: when coolant temperature is "warm" and ambient temperature is "warm", the starting condition is "met"; Rule 5: when coolant temperature is "very warm" and ambient temperature is "hot", the starting condition is "highly met"; S64. Fuzzification of input variables: convert the actual measured coolant temperature, ambient temperature and heating device state into corresponding fuzzy set membership degrees; S65. Rule evaluation: evaluate each rule and calculate the fuzzy set membership degree of the output result of each rule; S66. Output variable de-masking: using the weighted average method to combine the output results of multiple rules, and integrate the formula: , according to the formula to make the final start condition judgment; S67. Start judgment: starting condition "not met", "possibly not met": continue heating and re-evaluate regularly, starting condition "possibly met": prompt user to choose to start or wait for a period of time before starting, starting condition "met", "highly met": allow starting and record relevant data of this start; S68. Feedback and learning: obtain new input data: coolant temperature, ambient temperature, heater state in real time, and continuously adjust the starting judgment according to the above steps, the system needs to re-fuzzify, reason, de-fuzzify and judge every time the sensor reads new data, so as to make a start decision.

4. A control system for an engine coolant assisted start heating characterized by, The control system for engine coolant auxiliary start heating is used to realize the control method for engine coolant auxiliary start heating according to any one of claims 1-3; the control system for 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 acquires ambient temperature, coolant temperature and heating device state in real time through sensors, and sends the data to the fuzzy logic reasoning module and the PID control module; The fuzzy logic reasoning and PID control module: the fuzzy logic module makes a judgment on whether to start according to the ambient temperature, coolant temperature and heater state, and the PID controller adjusts the power of the heater in real time to ensure that the coolant temperature gradually rises; The heating process optimization and control module optimizes the heating time and power adjustment based on the decision tree regression model to ensure that the heating process is efficient and smooth; The judgment and decision module: the system judges whether the starting condition is met according to the fuzzy logic, and starts the engine when the coolant reaches the starting temperature; when it does not reach, continue heating. Safety and fault protection module: The system monitors the heater and power supply device in real time to ensure safe operation. When a fault occurs, the system will issue a warning and take protective measures.

Images