New energy automobile thermal management system

Abstract

The invention relates to the technical field of new energy, and discloses a new energy automobile heat management system which comprises the steps of obtaining heat production parameters of a core component through a multi-parameter sensing module, predicting the load increase and decrease amplitude, associating the whole automobile operation state parameters, calculating the automobile operation heat coefficient, obtaining system heat exchange state parameters through a basic modeling unit, and obtaining the heat exchange state parameters of the system. A refrigerant dryness prediction model is established, refrigeration demand increase and decrease are pre-judged, environment related parameters are collected, an air load change index is pre-judged, an opening demand index is calculated by combining an optimization regulation and control module with a calculation result of a multi-parameter sensing module, and an opening instruction is output after a regulation and control strategy is matched according to an opening demand index calculation result through a strategy execution module. According to the system, through full-chain parameter advanced sensing from a heat production source to environment influence, lagged feedback adjustment is converted into advanced trend regulation, finally, real-time matching of the opening degree of the expansion valve and heat load changes is achieved, and the problem of adjustment lagging is solved.

Description

New energy vehicle thermal management system Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a thermal management system for new energy vehicles. Background Technology

[0002] The electrification process in the automotive industry is progressing rapidly, shifting from gasoline and natural gas vehicles to hybrid and pure electric vehicles. Due to the different power sources, the thermal management systems of different powertrain types also differ. The thermal management of hybrid and pure electric vehicles differs significantly from that of traditional gasoline and natural gas vehicles. In traditional gasoline and natural gas vehicles, the thermal management subsystems are independent with little interconnection or integration. In contrast, the thermal management subsystems of hybrid and pure electric vehicles are integrated. Many components can be shared among these systems, energy can be exchanged, and control systems can be integrated.

[0003] The cooling process of a thermal management system in new energy vehicles mainly relies on the boiling heat exchange of the refrigerant within the pipes. However, this process depends on the flow pattern of the refrigerant's gas-liquid two-phase flow and its dryness. When the refrigerant's superheat is too high, meaning it evaporates prematurely in the middle or rear of the evaporator, the refrigerant's boiling heat exchange capacity is severely reduced. This problem directly affects the cooling effect of the thermal management system, making it difficult to stably control the temperature of core components such as the battery and electronic control system. This is a common problem faced by thermal management systems using similar refrigerant cooling solutions in the industry.

[0004] In a thermal management system, the opening of the expansion valve has a certain lag. From the change in the opening of the expansion valve to the change in the refrigerant flow, and then to the change in the superheat at the evaporator outlet, this is a process with a significant pure lag and time constant. When the PID detects that the superheat is too high, it is already too late. By the time it starts to act, the heat load may have changed again. Summary of the Invention

[0005] (I) Technical problems to be solved In view of the shortcomings of the existing technology, the present invention provides a thermal management system for new energy vehicles, which can transform the lagging feedback regulation into the advanced trend regulation by sensing parameters of the entire chain from the heat source to the environmental impact, and finally realize the real-time matching of the expansion valve opening and the change of heat load, thus solving the problem of regulation lag.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a new energy vehicle thermal management system, comprising a multi-parameter sensing module, an optimization and control module, and a strategy execution module; the multi-parameter sensing module includes a load prediction unit, a basic modeling unit, and a linkage prediction unit; the load prediction unit is used to acquire heat generation parameters of core components, predict the increase or decrease in load, and correlate with vehicle operating status parameters to calculate the vehicle operating heat coefficient; the basic modeling unit is used to acquire system heat exchange status parameters, establish a refrigerant dryness prediction model, and predict the increase or decrease in cooling demand; the linkage prediction unit is used to collect environmental correlation parameters and predict the air load change index; the optimization and control module is used to calculate the opening demand index by combining the calculation results of the multi-parameter sensing module; the strategy execution module is used to output the opening command after matching the control strategy according to the calculation results of the opening demand index.

[0007] Preferably, the core component heat generation parameters include the real-time charging and discharging power of the battery pack, the average temperature of the battery pack cells, the comprehensive operating current of the motor, and the temperature of the core module; the vehicle operating status parameters include vehicle speed and climbing load; the system heat exchange status parameters include the refrigerant temperature and pressure at the evaporator inlet and outlet, the air inlet and outlet temperatures and wind speeds, the refrigerant temperature and pressure at the condenser inlet and outlet, the air inlet and outlet temperatures and wind speeds, and the compressor operating frequency; the environmental related parameters include ambient temperature and solar radiation intensity.

[0008] Preferably, the specific parameters among the core component heat generation parameters, vehicle operating status parameters, system heat exchange status parameters, and environmental correlation parameters are normalized, and then each normalized parameter is represented by a normalized value.

[0009] Preferably, the load increase / decrease range The calculation formula is: ;In the formula, This represents the normalized value of the real-time charging and discharging power of the battery pack. This represents the normalized value of the average temperature of the battery pack cells. The normalized value representing the overall operating current of the motor. The normalized value representing the temperature of the core module; This represents the normalized value of the real-time charging and discharging power of the battery pack under baseline operating conditions. This represents the normalized value of the average temperature of the battery pack cells under baseline operating conditions. This represents the normalized value of the motor's overall operating current under baseline operating conditions. This represents the normalized value of the core module temperature under baseline operating conditions. The weight representing the real-time charging and discharging power of the battery pack. The weight representing the average temperature of the battery pack cells. The weight representing the overall operating current of the motor. The weight representing the temperature of the core module; .

[0010] Preferably, the vehicle's operating heat coefficient The calculation formula is: ;In the formula, Represents actual vehicle speed. Represents standard vehicle speed. Represents the vehicle speed correction factor. Represents the climbing load. Represents the maximum climbing load. Represents the slope correction factor; , Represents weight, and .

[0011] Preferably, the mathematical expression formula of the refrigerant dryness prediction model is: ;In the formula, Represents the refrigerant side weighting coefficient. This represents the refrigerant temperature at the evaporator outlet. This represents the saturation temperature corresponding to the evaporator outlet pressure. Represents the refrigerant temperature at the evaporator inlet. This represents the saturation temperature corresponding to the evaporator inlet pressure. Represents the actual operating frequency of the compressor. This represents the compressor's rated frequency; Represents the air-side weighting coefficient. This represents the air intake temperature on the evaporator side. This represents the air outlet temperature on the evaporator side. Represents the air velocity on the evaporator side; This represents the refrigerant pressure at the condenser outlet. This represents the refrigerant pressure at the evaporator outlet. This represents the error compensation coefficient.

[0012] Preferably, the air load change index The calculation formula is: ;In the formula, Represents ambient temperature. This represents the set temperature inside the car. Represents the reference temperature difference. Represents solar radiation intensity. Represents reference solar radiation intensity. , Represents weight, and .

[0013] Preferably, the opening degree demand index The calculation formula is: ;In the formula, This represents the minimum load increase or decrease. This represents the maximum increase or decrease in load. Represents the maximum vehicle operating heat coefficient. Represents the target refrigerant dryness. Represents the minimum air load change index. Represents the maximum air load change index. , , , Represents weight, and .

[0014] Preferably, the control strategy includes low opening demand, medium opening demand, and high opening demand.

[0015] Preferably, when For applications requiring low valve opening, the valve needs to be closed slightly. For medium opening requirements, maintain the current valve status; when To meet the high opening requirements, the valve needs to be opened wider.

[0016] Compared with existing technologies, this invention provides a thermal management system for new energy vehicles, which has the following beneficial effects: 1. This invention obtains the heat generation parameters of core components through a multi-parameter sensing module, calculates the load increase or decrease through formulas, compares the difference between the current parameters and the baseline operating conditions, and quantifies the upward or downward trend of the heat generation load. At the same time, combined with vehicle speed and climbing load, it calculates the vehicle operating heat coefficient through formulas, reflecting the amplification effect of increased vehicle speed and climbing load on heat generation. It can directly correlate the real-time heat generation status and operating enhancement trend of core components, without waiting for refrigerant heat exchange or superheat changes. It can predict in advance whether the future heat load will increase or decrease, providing an advanced basis for subsequent adjustment. Then, through the system heat exchange status parameters, a refrigerant dryness prediction model is established to predict the increase or decrease of cooling demand. It integrates refrigerant-side and air-side parameters. When the superheat has not deviated significantly from the target value, it can predict whether the refrigerant is about to become too dry or too wet through dryness changes. By collecting environmental related parameters, it predicts the air load change index, and captures environmental heat load fluctuations in advance to avoid adjustment lag when environmental changes cause sudden changes in heat load.

[0017] 2. This invention optimizes the control module and strategy execution module by integrating load increase / decrease, vehicle operating heat coefficient, refrigerant dryness, and air load change index to quantify the current demand for expansion valve opening. It transforms scattered leading information into a single control index, enabling the judgment of control demand to precede the actual change in heat load. Finally, after matching the control strategy, the opening command is output, avoiding the problem of delayed adjustment when traditional PID relies on superheat feedback. Ultimately, it achieves real-time matching between expansion valve opening and heat load changes, solving the problem of control lag. Attached Figure Description

[0018] Figure 1 is a schematic diagram of the system of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please refer to Figure 1. The new energy vehicle thermal management system includes a multi-parameter sensing module, an optimization and control module, and a strategy execution module. The multi-parameter sensing module includes a load prediction unit, a basic modeling unit, and a linkage prediction unit. The load prediction unit is used to acquire the heat generation parameters of core components, predict the load increase or decrease, and correlate them with the vehicle's operating status parameters to calculate the vehicle's operating heat coefficient. The heat generation parameters of core components include the real-time charging and discharging power of the battery pack, the average temperature of the battery pack cells, the comprehensive operating current of the motor, and the temperature of the core module. The core module temperature is taken as the highest temperature of the MCU / motor. The vehicle's operating status parameters include vehicle speed and climbing load. The basic modeling unit is used to acquire the system's heat exchange status parameters. A refrigerant dryness prediction model is established to predict increases and decreases in cooling demand. A linked prediction unit is used to collect environmental parameters and predict air load change indices. System heat exchange status parameters include refrigerant temperature, pressure, air inlet and outlet temperatures and velocities at the evaporator inlet and outlet, and refrigerant temperature, pressure, air inlet and outlet temperatures and velocities at the condenser inlet and outlet, as well as compressor operating frequency. Environmental parameters include ambient temperature and solar radiation intensity. The specific parameters in the core component heat generation parameters, vehicle operating status parameters, system heat exchange status parameters, and environmental parameters are normalized, and each normalized parameter is then represented by a normalized value. Load increase / decrease magnitude... The calculation formula is: ;In the formula, This represents the normalized value of the real-time charging and discharging power of the battery pack. This represents the normalized value of the average temperature of the battery pack cells. The normalized value representing the overall operating current of the motor. The normalized value representing the temperature of the core module; This represents the normalized value of the real-time charging and discharging power of the battery pack under baseline operating conditions. This represents the normalized value of the average temperature of the battery pack cells under baseline operating conditions. This represents the normalized value of the motor's overall operating current under baseline operating conditions. This represents the normalized value of the core module temperature under baseline operating conditions. The weight representing the real-time charging and discharging power of the battery pack. The weight representing the average temperature of the battery pack cells. The weight representing the overall operating current of the motor. The weight representing the temperature of the core module; ; The quantitative indicator, which integrates the normalized expected weights of the real-time charging and discharging power of the battery pack, the average temperature of the battery pack cells, the comprehensive operating current of the motor, and the temperature of the core module, reflects the current actual heat load of the system. The reference load is a quantified value of the typical total heat load during stable system operation, used to compare changes in the current load. This represents the difference between the current total heat production load and the baseline load, directly reflecting the increase or decrease in load relative to the baseline state. The formula is based on real-time heat production parameters of core components, which are normalized to eliminate dimensional differences. The current total heat production load is obtained through weighted summation, and then compared with the baseline load to calculate the relative change ratio, which is the load increase or decrease. It directly reflects changes in cooling demand, avoiding the lag of traditional "post-event feedback," and allows the expansion valve opening to match load changes in real time, preventing premature refrigerant drying or insufficient cooling; the vehicle's operating heat coefficient The calculation formula is: ;In the formula, Represents actual vehicle speed. Represents standard vehicle speed. Represents the vehicle speed correction factor. Represents the climbing load. Represents the maximum climbing load. Represents the slope correction factor; , Represents weight, and ; The relative ratio of vehicle speed represents the proportion of deviation between the actual vehicle speed and the standard vehicle speed, reflecting the trend of the influence of vehicle speed on heat generation at a basic level. The relative ratio of climbing load to maximum climbing load represents the proportion of actual climbing load to maximum climbing load, reflecting the trend of the impact of climbing intensity on heat generation at a basic level; vehicle operating heat coefficient. By integrating the effects of two key operating parameters—vehicle speed and ramp load—into a unified coefficient, the chaotic control caused by multiple dispersed parameters is avoided. This enables rapid prediction of increases or decreases in cooling demand, providing a concise and effective input basis for calculating opening commands. The mathematical expression formula for the refrigerant dryness prediction model is as follows: ;In the formula, Represents the refrigerant side weighting coefficient. This represents the refrigerant temperature at the evaporator outlet. This represents the saturation temperature corresponding to the evaporator outlet pressure. Represents the refrigerant temperature at the evaporator inlet. This represents the saturation temperature corresponding to the evaporator inlet pressure. Represents the actual operating frequency of the compressor. This represents the compressor's rated frequency; Represents the air-side weighting coefficient. This represents the air intake temperature on the evaporator side. This represents the air outlet temperature on the evaporator side. Represents the air velocity on the evaporator side; This represents the refrigerant pressure at the condenser outlet. This represents the refrigerant pressure at the evaporator outlet. Represents the error compensation coefficient; It represents the superheat of the refrigerant at the inlet and outlet of the evaporator, reflecting the degree of refrigerant conversion from liquid to gaseous state; The air load variation index represents the ratio of air-side heat exchange to system pressure difference, reflecting the impact of air load on refrigerant evaporation. The calculation formula is: ;In the formula, Represents ambient temperature. This represents the set temperature inside the car. Represents the reference temperature difference. Represents solar radiation intensity. Represents reference solar radiation intensity. , Represents weight, and The optimization and control module is used to combine the calculation results of the multi-parameter sensing module to calculate the opening demand index. The calculation formula is: ;In the formula, This represents the minimum load increase or decrease. This represents the maximum increase or decrease in load. Represents the maximum vehicle operating heat coefficient. Represents the target refrigerant dryness. Represents the minimum air load change index. Represents the maximum air load change index. , , , Represents weight, and Opening Demand Index By integrating disparate parameters, including the load increase / decrease of heat generation from core components, vehicle operating heat coefficient, refrigerant dryness fraction, and air load variation index, into a single value, valve opening adjustments can be predicted in advance. This avoids the lag that occurs when adjusting valves based on only one parameter, and reduces the frequent valve switching that may result from relying on a single parameter, causing drastic fluctuations in system pressure and flow, increasing energy consumption and component wear. This approach is suitable for the complex and variable operating conditions of vehicles. The strategy execution module calculates the opening demand index, matches it with the control strategy, and outputs the opening command. The control strategies include low opening demand, medium opening demand, and high opening demand. For applications requiring low valve opening, the valve needs to be closed slightly. For medium opening requirements, maintain the current valve status; when To meet the high opening requirements, the valve needs to be opened wider.

[0021] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A thermal management system for new energy vehicles, characterized in that: It includes a multi-parameter sensing module, an optimization and control module, and a strategy execution module. The multi-parameter sensing module includes a load prediction unit, a basic modeling unit, and a linkage prediction unit. The load prediction unit is used to acquire the heat generation parameters of core components, predict the increase or decrease of load, and correlate them with the vehicle's operating status parameters to calculate the vehicle's operating heat coefficient. The basic modeling unit is used to acquire system heat exchange status parameters, establish a refrigerant dryness prediction model, and predict the increase or decrease of cooling demand. The linkage prediction unit is used to collect environmental correlation parameters and predict the air load change index. The optimization and control module is used to calculate the opening demand index by combining the calculation results of the multi-parameter sensing module. The strategy execution module is used to calculate the opening demand index, match the control strategy, and then output the opening command.

2. The new energy vehicle thermal management system according to claim 1, characterized in that: The core component heat generation parameters include the real-time charging and discharging power of the battery pack, the average temperature of the battery pack cells, the comprehensive operating current of the motor, and the temperature of the core module; the vehicle operating status parameters include vehicle speed and climbing load; the system heat exchange status parameters include the refrigerant temperature and pressure at the evaporator inlet and outlet, the air inlet and outlet temperatures and wind speeds, the refrigerant temperature and pressure at the condenser inlet and outlet, the air inlet and outlet temperatures and wind speeds, and the compressor operating frequency; the environmental related parameters include ambient temperature and solar radiation intensity.

3. The new energy vehicle thermal management system according to claim 2, characterized in that: The specific parameters among the core component heat generation parameters, vehicle operating status parameters, system heat exchange status parameters, and environmental correlation parameters are normalized, and then each normalized parameter is represented by a normalized value.

4. The new energy vehicle thermal management system according to claim 3, characterized in that: The load increase / decrease The calculation formula is: ; in the official, This represents the normalized value of the real-time charging and discharging power of the battery pack. This represents the normalized value of the average temperature of the battery pack cells. The normalized value representing the overall operating current of the motor. The normalized value representing the temperature of the core module; This represents the normalized value of the real-time charging and discharging power of the battery pack under baseline operating conditions. This represents the normalized value of the average temperature of the battery pack cells under baseline operating conditions. This represents the normalized value of the motor's overall operating current under baseline operating conditions. This represents the normalized value of the core module temperature under baseline operating conditions. The weight representing the real-time charging and discharging power of the battery pack. The weight representing the average temperature of the battery pack cells. The weight representing the overall operating current of the motor. The weight representing the temperature of the core module; 。 5. The new energy vehicle thermal management system according to claim 3, characterized in that: The vehicle operating heat coefficient The calculation formula is: ; in the official, Represents actual vehicle speed. Represents standard vehicle speed. Represents the vehicle speed correction factor. Represents the uphill load. Represents the maximum climbing load. Represents the slope correction factor; ， Represents weight, and 。 6. The new energy vehicle thermal management system according to claim 3, characterized in that: The mathematical expression of the refrigerant dryness prediction model is as follows: ; in the official, Represents the refrigerant side weighting coefficient. This represents the refrigerant temperature at the evaporator outlet. This represents the saturation temperature corresponding to the evaporator outlet pressure. Represents the refrigerant temperature at the evaporator inlet. This represents the saturation temperature corresponding to the evaporator inlet pressure. Represents the actual operating frequency of the compressor. This represents the compressor's rated frequency; Represents the air-side weighting coefficient. This represents the air intake temperature on the evaporator side. This represents the air outlet temperature on the evaporator side. Represents the air velocity on the evaporator side; This represents the refrigerant pressure at the condenser outlet. This represents the refrigerant pressure at the evaporator outlet. This represents the error compensation coefficient.

7. The new energy vehicle thermal management system according to claim 3, characterized in that: The air load change index The calculation formula is: ; in the official, Represents ambient temperature. This represents the set temperature inside the car. Represents the reference temperature difference. Represents solar radiation intensity. Represents reference solar radiation intensity. 、 Represents weight, and 。 8. The new energy vehicle thermal management system according to claim 3, characterized in that: The opening demand index The calculation formula is: ; in the official, This represents the minimum load increase or decrease. This represents the maximum increase or decrease in load. Represents the maximum vehicle operating heat coefficient. Represents the target refrigerant dryness. Represents the minimum air load change index. Represents the maximum air load change index. 、 、 、 Represents weight, and 。 9. The new energy vehicle thermal management system according to claim 8, characterized in that: The control strategies include low opening demand, medium opening demand, and high opening demand.

10. The new energy vehicle thermal management system according to claim 9, characterized in that: when For applications requiring low valve opening, the valve needs to be closed slightly. For medium opening requirements, maintain the current valve status; when To meet the high opening requirements, the valve needs to be opened wider.

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