A new energy vehicle thermal management system heat load prediction method

Abstract

The present application relates to the field of new energy vehicle thermal management technology, and belongs to a new energy vehicle thermal management system thermal load prediction method.According to different thermal management task requests, the present application sets up differentiated thermal load calculation strategies, which can adaptively match the actual operating conditions of the vehicle.In terms of passenger cabin thermal load calculation, the present application introduces three calculation dimensions of vehicle speed, air conditioner air volume and air intake circulation ratio to perform multi-factor collaborative correction on the basic thermal load of the passenger cabin, so that the calculated passenger cabin thermal load is closer to the dynamic change characteristics in the real physical environment.In terms of battery thermal load calculation, the present application introduces key parameters such as battery charging and discharging state, current, voltage and liquid cooling plate efficiency factor, and divides the battery heat generation into static thermal load and dynamic thermal load for separate calculation, so as to accurately reflect the overall instantaneous heat generation state of the battery under the current operating condition.

Description

Technical Field

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

[0002] With the rapid development of the new energy vehicle industry and the continuous increase in market penetration, car consumers are increasingly demanding higher standards for vehicle comfort and operational safety. As a key system connecting the overall vehicle thermal environment and the temperature safety of core components, the thermal management system of new energy vehicles undertakes a dual core mission: on the one hand, it needs to provide a suitable temperature and humidity environment for the passenger compartment to ensure driving comfort; on the other hand, it needs to perform precise temperature control on the core three-electric systems such as the power battery to ensure that they operate within the optimal operating temperature range and prevent safety hazards caused by overheating or performance degradation caused by overcooling.

[0003] The effectiveness of a thermal management system highly depends on the real-time and accurate perception of the vehicle's thermal load. Specifically, the system needs to acquire the thermal load demands of both the passenger compartment and the battery to accurately calculate the required cooling or heating capacity, and then coordinate the control of components such as the compressor, electronic expansion valve, and water pump. If the thermal load estimate is too low, it will result in insufficient cooling, reduced passenger comfort, or uncontrolled battery temperature; if the estimate is too high, it will lead to excessive cooling, increased unnecessary energy consumption, and a shortened driving range. Therefore, achieving accurate and real-time prediction of vehicle thermal load has become a core technical challenge that urgently needs to be overcome in the field of thermal management for new energy vehicles.

[0004] However, the actual operating scenarios of new energy vehicles are extremely complex and variable, posing a significant challenge to the accurate prediction of heat load. On the passenger compartment side, real-time changes in vehicle speed directly affect the intensity of convective heat transfer outside the vehicle, the airflow of the air conditioner alters the airflow and heat transfer characteristics inside the vehicle, and the switching between internal and external air circulation modes determines the proportion of fresh air introduced and the corresponding fresh air heat load. These three factors are coupled and dynamically change, resulting in a highly time-varying and nonlinear heat load in the passenger compartment. On the battery side, factors such as the battery's state of charge / discharge (charging or discharging), charge / discharge rate (current magnitude), voltage plateau, and state of charge (SOC) all significantly affect the battery's own heating characteristics. Especially under rapid charging or high-power discharging conditions, the battery's heat generation rate increases sharply, and the heat load exhibits drastic fluctuations.

[0005] Currently, most mainstream heat load algorithms in the industry employ simplified models or fixed-parameter estimation methods based on steady-state operating conditions. While these traditional solutions can meet basic needs in relatively simple scenarios such as constant-speed driving and steady-state air conditioning, their prediction accuracy significantly decreases and response lag becomes apparent when faced with complex and variable usage scenarios such as frequent acceleration and deceleration, switching between charging and discharging states, and dynamic adjustments to air conditioning modes. They also struggle to track the dynamic changes in vehicle heat load in real time. Although some existing technologies introduce correction coefficients, the correction dimensions are relatively singular, failing to comprehensively cover the combined impact of multiple factors such as vehicle speed, airflow, and circulation mode on passenger compartment load. Regarding battery heat load calculation, most solutions do not effectively distinguish and separate the static thermal property-induced heat generation from the dynamic electrochemical heat generation, leading to significant discrepancies between estimated and actual heat generation. These shortcomings ultimately prevent the thermal management system from accurately matching actual cooling needs, either sacrificing comfort and safety or causing unnecessary energy loss. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method for predicting the heat load of a thermal management system for new energy vehicles. This method can calculate the heat load of the passenger compartment and battery side of the vehicle in a timely and accurate manner, providing a basis for the air conditioner to provide the corresponding cooling capacity in real time, thus ensuring the comfort and safety of the vehicle.

[0007] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a method for predicting the heat load of a thermal management system for new energy vehicles, specifically including: Step S1: Obtain the vehicle's thermal management task request; Step S2: Based on the type of the thermal management task request, determine the corresponding total heat load prediction method, wherein the total heat load prediction method includes at least one of the passenger cabin heat load prediction method and the battery heat load prediction method; Step S3: According to the corresponding total heat load algorithm determined in step S2, obtain the real-time vehicle operating status parameters and environmental parameters that match the type of the current thermal management task request; Step S4: Calculate the total heat load value of the vehicle based on the vehicle operating parameters described in step S3, and dynamically adjust the compressor speed based on the total heat load value.

[0008] Preferably, in the passenger cabin heat load prediction method, the operating status parameters include blower speed, vehicle speed, air conditioning air volume, and internal circulation ratio. In the battery heat load prediction method, the operating status parameters include battery charge / discharge status, battery current and battery voltage, coolant flow rate, maximum battery cell temperature, and target battery coolant temperature.

[0009] Preferably, in step S2, the correspondence between the type of thermal management task request and the total heat load prediction method is as follows: When only passenger compartment thermal management is requested by the vehicle: The total heat load of the vehicle is equal to the heat load of the passenger compartment, and the prediction method for the overall heat load of the vehicle is the same as that for the passenger compartment heat load. When the vehicle only requests battery thermal management: The total heat load of the vehicle is equal to the total heat load of the battery, and the prediction method for the overall heat load of the vehicle is the same as that for the battery heat load. When the vehicle passenger compartment and battery collaborative thermal management request: The total heat load of the vehicle is the sum of the heat load value of the passenger compartment and the total heat load value of the battery. The heat load value of the passenger compartment and the total heat load value of the battery are calculated by corresponding prediction methods.

[0010] Preferably, the passenger cabin heat load prediction method is as follows: S211. Based on the blower speed and a preset mapping relationship between speed and air volume, determine the air conditioning air volume; S212. Based on the vehicle speed, determine the intake weight coefficient according to the preset mapping relationship between the vehicle speed and the internal circulation ratio. S213. Based on the intake weight coefficient obtained in step S212, and combined with the vehicle interior temperature and vehicle exterior temperature, calculate the evaporator intake temperature. ; Where f: internal recirculation intake weight coefficient, obtained by calibration test according to the mapping relationship between vehicle speed and internal recirculation ratio, and the value of f is limited to [0, 1]; S214. Calculate the inlet and outlet air temperature difference based on the evaporator inlet temperature obtained in step S213 and the target outlet air temperature. ; in, The expected air temperature blown out of the air conditioner vent will be obtained through test calibration. S215. Based on the air conditioning air volume obtained in step S211 and the inlet and outlet air temperature difference obtained in step S214, and combined with the air density and constant pressure specific heat capacity, the predicted value of passenger cabin heat load is obtained.

[0011] ; in, Air conditioner blower volume and air volume , air density , Specific heat capacity of air at constant pressure .

[0012] Preferably, the battery heat load prediction method is as follows: S221. Determine the static heat load component of the battery based on the coolant flow rate, the highest temperature of the battery cell, and the target coolant temperature of the battery. The static heat load component represents the heat carried away from the battery by the coolant. S222. Determine the dynamic heat load component of the battery based on the battery current, the battery voltage, and a preset efficiency factor. The dynamic heat load component characterizes the electrochemical heat generation of the battery. S223. The static heat load component is superimposed with the dynamic heat load component to obtain the total heat load value of the battery.

[0013] Preferably, the static heat load component is calculated using the following formula: ; in, : Battery static heat load component; Coolant flow rate through the battery In the early stages, test data maps were obtained through whole-vehicle flow rate testing. Different water pump speeds correspond to different coolant flow rates, which can be looked up in the table during use. Density of coolant flowing through the battery For ease of calculation, a constant is usually taken based on the specific coolant type. Specific heat capacity of coolant flowing through the battery For ease of calculation, a constant is usually taken based on the specific coolant type. = - ; Battery temperature difference; : Maximum temperature of battery cell; Target coolant temperature for the battery.

[0014] Preferably, the dynamic heat load component is calculated using the following formula: in, Battery dynamic thermal load; : Take the absolute value sign; Battery bus current; : Battery high voltage; Efficiency factor.

[0015] Compared with existing technologies, this invention provides a method for predicting the heat load of a thermal management system for new energy vehicles, which has the following beneficial effects: This invention sets differentiated heat load calculation strategies based on different thermal management task requests, enabling adaptive matching to the actual operating conditions of the vehicle. Specifically, in terms of passenger compartment heat load calculation, this invention introduces three calculation dimensions: vehicle speed, air conditioning airflow, and intake air recirculation ratio. This allows for multi-factor collaborative correction of the passenger compartment's basic heat load, making the calculated passenger compartment heat load more closely reflect the dynamic changes in the real physical environment. In terms of battery heat load calculation, this invention incorporates key parameters such as battery charge / discharge status, current, voltage, and liquid cooling plate efficiency factor. It distinguishes between static and dynamic heat loads for separate calculation, thereby reflecting the overall instantaneous heating state of the battery under current operating conditions in real time and accurately. Through the aforementioned collaborative heat load calculation mechanism on both the passenger compartment and battery sides, this invention can timely and accurately obtain the vehicle's total heat load demand at any given moment for complex and ever-changing real-world vehicle operating scenarios. Furthermore, by adjusting the compressor output to the corresponding target cooling capacity in real time, it achieves on-demand cooling. Compared with existing technologies, this invention improves passenger cabin comfort and battery thermal safety while effectively avoiding energy waste caused by excessive cooling capacity, providing reliable data support for the intelligent and refined control of the vehicle thermal management system.

[0016] The features and advantages of the present invention will be described in detail through embodiments and in conjunction with the accompanying drawings. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for predicting the heat load of a new energy vehicle thermal management system according to the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0019] See Figure 1 This embodiment provides a method for predicting the heat load of a new energy vehicle thermal management system, including the following steps: Step S1: Obtain the vehicle's thermal management task request; Step S2: Based on the type of the thermal management task request, determine the corresponding total heat load prediction method, wherein the total heat load prediction method includes at least one of the passenger cabin heat load prediction method and the battery heat load prediction method; Step S3: According to the corresponding total heat load algorithm determined in step S2, obtain the real-time vehicle operating status parameters and environmental parameters that match the type of the current thermal management task request; Step S4: Calculate the total heat load value of the vehicle based on the vehicle operating parameters described in step S3, and dynamically adjust the compressor speed based on the total heat load value.

[0020] Preferably, in the passenger cabin heat load prediction method, the operating status parameters include blower speed, vehicle speed, air conditioning air volume, and internal circulation ratio. In the battery heat load prediction method, the operating status parameters include battery charge / discharge status, battery current and battery voltage, coolant flow rate, maximum battery cell temperature, and target battery coolant temperature.

[0021] Preferably, in step S2, the correspondence between the type of thermal management task request and the total heat load prediction method is as follows: When only passenger compartment thermal management is requested by the vehicle: The total heat load of the vehicle is equal to the heat load of the passenger compartment, and the prediction method for the overall heat load of the vehicle is the same as that for the passenger compartment heat load. When the vehicle only requests battery thermal management: The total heat load of the vehicle is equal to the total heat load of the battery, and the prediction method for the overall heat load of the vehicle is the same as that for the battery heat load. When the vehicle passenger compartment and battery collaborative thermal management request: The total heat load of the vehicle is the sum of the heat load value of the passenger compartment and the total heat load value of the battery. The heat load value of the passenger compartment and the total heat load value of the battery are calculated by corresponding prediction methods.

[0022] Preferably, the passenger cabin heat load prediction method is as follows: S211. Based on the blower speed and a preset mapping relationship between speed and air volume, determine the air conditioning air volume; S212. Based on the vehicle speed, determine the intake weight coefficient according to the preset mapping relationship between the vehicle speed and the internal circulation ratio. S213. Based on the intake weight coefficient obtained in step S212, and combined with the vehicle interior temperature and vehicle exterior temperature, calculate the evaporator intake temperature. ; Where f: internal recirculation intake weight coefficient, obtained by calibration test according to the mapping relationship between vehicle speed and internal recirculation ratio, and the value of f is limited to [0, 1]; The mapping relationship between vehicle speed and the internal circulation ratio can be represented as follows: S214. Calculate the inlet and outlet air temperature difference based on the evaporator inlet temperature obtained in step S213 and the target outlet air temperature. ; in, The expected air temperature blown out of the air conditioner vent will be obtained through test calibration. S215. Based on the air conditioning air volume obtained in step S211 and the inlet and outlet air temperature difference obtained in step S214, and combined with the air density and constant pressure specific heat capacity, the predicted value of passenger cabin heat load is obtained.

[0023] ; in, Air conditioner blower volume and air volume , air density , Specific heat capacity of air at constant pressure .

[0024] Preferably, the battery heat load prediction method is as follows: S221. Determine the static heat load component of the battery based on the coolant flow rate, the highest temperature of the battery cell, and the target coolant temperature of the battery. The static heat load component represents the heat carried away from the battery by the coolant. S222. Determine the dynamic heat load component of the battery based on the battery current, the battery voltage, and a preset efficiency factor. The dynamic heat load component characterizes the electrochemical heat generation of the battery. S223. The static heat load component is superimposed with the dynamic heat load component to obtain the total heat load value of the battery.

[0025] Preferably, the static heat load component is calculated using the following formula: ; in, : Battery static heat load component; Coolant flow rate through the battery In the early stages, test data maps were obtained through whole-vehicle flow rate testing. Different water pump speeds correspond to different coolant flow rates, which can be looked up in the table during use. Density of coolant flowing through the battery For ease of calculation, a constant is usually taken based on the specific coolant type. Specific heat capacity of coolant flowing through the battery For ease of calculation, a constant is usually taken based on the specific coolant type. = - ; Battery temperature difference; : Maximum temperature of battery cell; Target coolant temperature for the battery.

[0026] Preferably, the dynamic heat load component is calculated using the following formula: in, Battery dynamic thermal load; : Take the absolute value sign; Battery bus current; : Battery high voltage; Efficiency factor.

[0027] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting heat load in a thermal management system for new energy vehicles, characterized in that: Specifically, it includes: Step S1: Obtain the vehicle's thermal management task request; Step S2: Based on the type of the thermal management task request, determine the corresponding total heat load prediction method, wherein the total heat load prediction method includes at least one of the passenger cabin heat load prediction method and the battery heat load prediction method; Step S3: According to the corresponding total heat load algorithm determined in step S2, obtain the real-time vehicle operating status parameters and environmental parameters that match the type of the current thermal management task request; Step S4: Calculate the total heat load value of the vehicle based on the vehicle operating parameters described in step S3, and dynamically adjust the compressor speed based on the total heat load value.

2. The method for predicting heat load in a new energy vehicle thermal management system according to claim 1, characterized in that: In the passenger cabin heat load prediction method, the operating status parameters include blower speed, vehicle speed, air conditioning air volume, and internal circulation ratio. In the battery heat load prediction method, the operating status parameters include battery charge / discharge status, battery current and battery voltage, coolant flow rate, maximum battery cell temperature and target battery coolant temperature.

3. The method for predicting heat load in a new energy vehicle thermal management system according to claim 1, characterized in that: In step S2, the correspondence between the type of thermal management task request and the total heat load prediction method is as follows: When the vehicle only requests thermal management for the passenger compartment: The total heat load of the vehicle is equal to the heat load of the passenger compartment, and the prediction method for the overall heat load of the vehicle is the same as that for the passenger compartment heat load. When the vehicle only requests battery thermal management: The total heat load of the vehicle is equal to the total heat load of the battery, and the prediction method for the overall heat load of the vehicle is the same as that for the battery heat load. When the vehicle passenger compartment and battery collaborative thermal management request: The total heat load of the vehicle is the sum of the heat load value of the passenger compartment and the total heat load value of the battery. The heat load value of the passenger compartment and the total heat load value of the battery are calculated by corresponding prediction methods.

4. The method for predicting heat load in a new energy vehicle thermal management system according to claim 2, characterized in that: The specific method for predicting passenger cabin heat load is as follows: S211. Based on the blower speed and a preset mapping relationship between speed and air volume, determine the air conditioning air volume; S212. Based on the vehicle speed, determine the intake weight coefficient according to the preset mapping relationship between the vehicle speed and the internal circulation ratio. S213. Based on the intake weight coefficient obtained in step S212, and combined with the vehicle interior temperature and vehicle exterior temperature, calculate the evaporator intake temperature. S214. Calculate the inlet and outlet air temperature difference based on the evaporator inlet temperature obtained in step S213 and the target outlet air temperature. S215. Based on the air conditioning air volume obtained in step S211 and the inlet and outlet air temperature difference obtained in step S214, and combined with the air density and constant pressure specific heat capacity, the predicted value of passenger cabin heat load is obtained.

5. The method for predicting heat load in a new energy vehicle thermal management system according to claim 2, characterized in that: The specific method for predicting battery heat load is as follows: S221. Determine the static heat load component of the battery based on the coolant flow rate, the highest temperature of the battery cell, and the target coolant temperature of the battery. The static heat load component represents the heat carried away from the battery by the coolant. S222. Determine the dynamic heat load component of the battery based on the battery current, the battery voltage, and a preset efficiency factor. The dynamic heat load component characterizes the electrochemical heat generation of the battery. S223. The static heat load component is superimposed with the dynamic heat load component to obtain the total heat load value of the battery.

6. The method for predicting heat load in a new energy vehicle thermal management system according to claim 5, characterized in that: The static heat load component is calculated using the following formula: ； in, : Battery static heat load component; Coolant flow rate through the battery ; Density of coolant flowing through the battery ; Specific heat capacity of coolant flowing through the battery ; = - ; Battery temperature difference; : Maximum temperature of battery cell; Target coolant temperature for the battery.

7. The method for predicting heat load in a new energy vehicle thermal management system according to claim 5, characterized in that: The dynamic heat load component is calculated using the following formula: in, Battery dynamic thermal load; : Take the absolute value sign; Battery bus current; : Battery high voltage; Efficiency factor.

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