Low-energy-consumption double-evaporator parallel type heat management system for pure electric vehicle

Abstract

The invention discloses a low-energy-consumption double-evaporator parallel type thermal management system for a pure electric vehicle, and belongs to the technical field of thermal management of pure electric vehicles. The system comprises a data acquisition module, a load resolving module and a control execution module, the data acquisition module acquires heat management related data of the pure electric vehicle; the load resolving module is used for dividing three refrigeration working conditions according to trigger conditions, performing thermal load real-time quantification and thermal load prediction in a future period according to thermal management related data, then taking total energy consumption minimization as a target, combining preset temperature constraints to generate double-evaporator parallel control parameters, and entering a PID control temperature maintaining mode after the temperature reaches the standard; and the control execution module issues the parallel control parameters of the double evaporators to a corresponding actuator for execution. The system is used for refrigeration and cooling of the cabin and the battery of the pure electric vehicle during summer driving, low-energy-consumption heat management of refrigeration of the pure electric vehicle is achieved through precise regulation and control of working conditions, and the refrigeration precision and the system energy efficiency are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of thermal management technology for pure electric vehicles, and specifically to a low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles. Background Technology

[0002] Since pure electric vehicles lack engine waste heat, their thermal management systems rely entirely on electric power. During summer driving, cabin cooling and battery cooling become the main components of overall vehicle energy consumption, directly impacting the vehicle's range. Therefore, low-energy cooling and thermal management solutions have become a core focus of pure electric vehicle development. Currently, most pure electric vehicles employ single-evaporator or series-connected dual-evaporator cooling and thermal management systems. These systems cannot achieve independent control of cabin and battery cooling loads, are prone to low heat exchange efficiency due to mutual compromise in evaporation temperatures, and struggle to dynamically match cooling output to actual cooling needs. They generally suffer from high energy consumption and insufficient cooling precision, failing to balance cabin comfort with battery operating temperature stability.

[0003] To address the aforementioned issues, some technical solutions attempt to optimize the refrigeration and thermal management system using a parallel dual-evaporator structure. However, existing parallel solutions often lack precise real-time quantification of heat load and prediction of future time periods. The control algorithms only implement simple on / off switching and speed regulation, failing to combine the goal of minimizing total energy consumption with dynamic adjustment of the control objective function and temperature constraints based on operating conditions. Furthermore, once the temperature reaches the target, there is a lack of refined closed-loop maintenance strategies, resulting in issues such as wasted cooling capacity and redundant actuator control. This makes it difficult to achieve low-energy coordinated management of cabin cooling and battery cooling, and fails to meet the high-efficiency thermal management requirements of pure electric vehicles during summer driving. Summary of the Invention

[0004] This invention provides a low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles to solve the problems of high energy consumption, low accuracy, poor control of the dual-evaporator parallel scheme, and inability to manage cabin and battery cooling in a low-energy-consumption coordinated manner in existing pure electric vehicle cooling and thermal management systems.

[0005] To address the aforementioned problems, according to one aspect of the present invention, a low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles is disclosed, comprising a data acquisition module, a data acquisition module, and a load calculation module.

[0006] The data acquisition module is used to collect and store thermal management-related data of pure electric vehicles. The thermal management-related data includes heat load source data, temperature data, actuator status data, and cabin temperature setpoint.

[0007] The load calculation module is used to perform real-time quantification of the thermal load and prediction of the thermal load in future periods for the thermal management-related data. Then, with the goal of minimizing the total energy consumption, combined with the preset temperature constraints, the parallel control parameters of the dual evaporators are generated.

[0008] The control execution module is used to send the parallel control parameters of the dual evaporators to the corresponding actuators for execution.

[0009] Furthermore, the thermal load source data includes the number of cabin occupants, solar irradiance, vehicle speed, and battery charge and discharge current. The temperature data includes the actual cabin temperature, battery pack temperature, and external ambient temperature. The actuator status data includes the compressor speed, electronic expansion valve opening, and fan speed. All the thermal management-related data are collected in real time through the sensing and detection devices, vehicle CAN bus, and vehicle control unit supporting the pure electric vehicle, and then uniformly transmitted to the storage unit for storage. Among them, the number of cabin occupants is collected by the pressure sensor or infrared induction sensor configured on the cabin seat, the solar irradiance is collected by the solar irradiance sensor installed on the vehicle body roof, the vehicle speed is obtained from the vehicle controller VCU through the vehicle CAN bus, the battery charge and discharge current is collected by the current sensor supporting the battery management system BMS, the actual cabin temperature is collected by the temperature sensor arranged in the cabin, the battery pack temperature is collected by the NTC temperature sensor attached to the battery pack, and the external ambient temperature is collected by the ambient temperature sensor outside the vehicle. The compressor speed, fan speed, and electronic expansion valve opening are collected in real time by the vehicle air conditioning control unit and transmitted through the vehicle CAN bus. Among them, the electronic expansion valve opening includes the opening of electronic expansion valve EXV1 and the opening of electronic expansion valve EXV2, and the fan speed includes the cabin fan speed and the battery fan speed.

[0010] Furthermore, the triggering condition of the load calculation module is that there is at least one of the following situations: the cabin temperature deviation > ±0.5°C and the battery pack temperature > the preset battery pack temperature threshold. The cabin temperature deviation is the difference between the actual cabin temperature and the cabin temperature set value . The preset battery pack temperature threshold is a critical determination temperature value of the cooling requirement calibrated in advance according to the performance parameters and safe working range of the power battery pack, which is used to ensure that the power battery pack maintains an efficient working temperature range and avoid problems such as the attenuation of charge and discharge performance and the shortening of the cycle service life due to excessive temperature of the battery pack.

[0011] Furthermore, the real-time quantification of heat load is divided into quantified cabin cooling load and battery cooling load. The cabin cooling load is calculated by substituting the number of passengers, solar irradiance, vehicle speed, ambient temperature, and actual cabin temperature into the cabin cooling load formula. The battery cooling load is calculated by substituting the battery charging / discharging current, battery pack temperature, and ambient temperature into the battery cooling load formula. The future heat load prediction employs a Long Short-Term Memory (LSTM) algorithm, using cabin cooling load, battery cooling load, actual cabin temperature, battery pack temperature, ambient temperature, battery charging / discharging current, and number of passengers as input features, and outputs a predicted cabin cooling load value with a preset prediction step size. and battery cooling load forecast The prediction step size is 1 minute, and the preset number of prediction steps is 3-5.

[0012] The formula for cabin cooling load is:

[0013] ;

[0014] in, For cabin cooling load, For the number of passengers in the cabin, Solar irradiance, This refers to the total area of ​​the vehicle windows (a fixed calibration value for the vehicle). air density, The specific heat capacity of air at constant pressure. For fresh air heat exchange volumetric flow rate , The ambient temperature, This refers to the actual cabin temperature. The outer surface area of ​​the cabin (a fixed calibration value for the vehicle);

[0015] The formula for the battery cooling load is:

[0016] ;

[0017] in, For battery pack temperature, For battery charging and discharging current, This refers to the battery's internal resistance (a fixed calibration value for the power battery pack).

[0018] Furthermore, the parallel control parameters of the dual evaporators are controlled using a model predictive control (MPC) algorithm with respect to total energy consumption. Minimize the objective function, and combine actuator state data and cabin temperature constraints. Battery pack temperature constraints Preset battery pack temperature threshold generate;

[0019] The objective function is: ;

[0020] in, For compressor power, and , For cabin fan power, For battery fan power, For cabin fan power, This refers to the compressor's rated efficiency (calibrated value).

[0021] Furthermore, the load calculation module divides the cooling conditions into any one of cabin cooling conditions, battery cooling conditions, and coordinated cooling conditions based on the triggering conditions, with each cooling condition corresponding to different parallel control parameters for dual evaporators.

[0022] The trigger condition for the cabin cooling mode is as follows: and During cabin cooling operation, the battery evaporator branch and battery fan are shut down, i.e., the battery fan speed parameters are... With the EXV2 electronic expansion valve opening parameter set to 0, the cockpit fan speed is adjusted solely through a model predictive control algorithm. The opening parameters of the electronic expansion valve EXV1 and the compressor speed parameters are matched. The corresponding cooling demand is calculated by removing the battery cooling load prediction term from the objective function. and battery fan item make Minimum temperature constraint removal of battery pack temperature constraint;

[0023] The triggering condition for the battery cooling mode is as follows: and During cabin cooling operation, the battery evaporator branch and battery fan are shut down, i.e., the cabin fan speed parameters are... With the electronic expansion valve EXV1 opening parameter set to 0, the battery fan speed parameter is adjusted solely through a model predictive control algorithm. The opening parameters of the electronic expansion valve EXV2 and the compressor speed parameters are matched. The corresponding cooling demand is calculated, while the cabin cooling load forecast term in the objective function is removed. and cabin fan item make Minimum temperature constraint removal of battery pack temperature constraint;

[0024] The triggering condition for the dual-cooling coordinated operation is as follows: and Model predictive control algorithm matching under coordinated cooling conditions and The corresponding cooling demand, in terms of total energy consumption The objective function is minimized, and the complete output, including compressor speed parameters, electronic expansion valve EXV1 opening parameters, electronic expansion valve EXV2 opening parameters, cabin fan speed parameters, and battery fan speed parameters, is combined with cabin temperature constraints and battery pack temperature constraints to form the parallel control parameters for the dual evaporators.

[0025] Furthermore, the conditions for the load calculation module to enter the temperature maintenance mode are as follows: under cabin cooling conditions, the cabin temperature deviation after the parallel control parameters of the dual evaporators are sent to the corresponding actuators is ≤ ±0.5℃; under battery cooling conditions, the battery pack temperature after the parallel control parameters of the dual evaporators are sent to the corresponding actuators is ≤ a preset battery pack temperature threshold; under coordinated cooling conditions, the cabin temperature deviation after the parallel control parameters of the dual evaporators are sent to the corresponding actuators is ≤ ±0.5℃ and the battery pack temperature is ≤ a preset battery pack temperature threshold.

[0026] Furthermore, the temperature maintenance mode employs a closed-loop dynamic adjustment strategy using a dual-path independent PID closed-loop control algorithm. Independent and decoupled PID control loops are constructed for the cabin temperature and battery pack temperature respectively. Using the temperature deviation as input, the PID control algorithm generates corresponding actuator fine-tuning values, avoiding mutual interference between the two adjustments. The temperature deviation on the cabin side is... The temperature deviation on the battery side is The mathematical expression for the PID control algorithm of the corresponding cabin temperature PID control loop is: The mathematical expression for the PID control algorithm of the battery pack temperature PID control loop is as follows: In the formula, For cockpit-side proportional gain, For cockpit-side integral gain, For the cockpit-side differential gain, For battery-side proportional gain, For battery-side integral gain, For the battery-side differential gain, For battery-side actuator fine-tuning and For cabin-side actuator fine-tuning; the load calculation module outputs the PID control from both channels. and The adjustment is converted into small fine-tuning amounts for the compressor, electronic expansion valve EXV1, electronic expansion valve EXV2, cabin fan, and battery fan. Constraints are applied to the adjustment range of each actuator, wherein the fine-tuning amount of the electronic expansion valve opening does not exceed ±2%, the fine-tuning amount of the fan speed does not exceed ±50 r / min, and the total fine-tuning amount of the compressor speed does not exceed ±100 r / min. The control execution module sends the constrained actuator fine-tuning data to the corresponding actuator according to a preset adjustment cycle of 1-2 seconds, so as to achieve smooth actuator operation, stable system energy consumption, and no frequent shocks, thereby continuously maintaining the operating state with minimal total energy consumption while meeting the temperature constraint conditions.

[0027] Furthermore, the system is installed in the vehicle system of a pure electric vehicle and communicates with the vehicle control unit (VCU), battery management system (BMS), vehicle air conditioning control unit, and vehicle display unit. It is used for low-energy thermal management of cabin cooling and battery cooling in pure electric vehicles during summer driving. The system can only be put into operation after the driver actively selects to turn it on through the vehicle display unit. When not actively selected, the system is in standby hibernation state. If the driver does not set the cabin temperature setting value through the vehicle display unit, the system will automatically set the cabin temperature setting value to 26°C by default.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] 1. This invention overcomes the technical limitation of dual evaporator systems being unable to independently control the cabin and battery cooling loads. By accurately quantifying and calculating the cabin cooling load and battery cooling load, and combining it with a long short-term memory network algorithm, it achieves advanced prediction of heat load, avoiding the problem of low heat exchange efficiency caused by mutual accommodation of evaporation temperatures, and achieving precise matching between cooling capacity and actual cooling demand.

[0030] 2. This invention overcomes the shortcomings of existing parallel control schemes, which are simple and do not optimize energy consumption as needed. It combines model predictive control algorithm with the goal of minimizing total energy consumption, divides the operating conditions into three categories according to refrigeration demand, and dynamically adjusts the control objective function and temperature constraints to achieve decoupled independent control of the dual evaporator branches, thereby reducing the ineffective energy consumption of the refrigeration system from the root.

[0031] 3. This invention solves the problems of traditional solutions lacking a refined maintenance strategy after the temperature reaches the target, easily resulting in wasted cooling capacity and redundant actuator control. It designs a dual-path independent PID closed-loop temperature maintenance mode, which realizes high-precision steady-state regulation of cabin and battery temperatures, taking into account both cabin cooling comfort and battery operating temperature stability, and ultimately achieving low-energy collaborative management of cabin cooling and battery cooling in pure electric vehicles. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the system functional modules of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0035] In one embodiment, such as Figure 1 The invention relates to a low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles, comprising a data acquisition module, a control execution module, and a load calculation module. These modules work together to complete the thermal management data acquisition, thermal load quantification and prediction, control parameter optimization and generation, and actuator precise control of the cabin and battery pack during summer driving of pure electric vehicles. This achieves low-energy-consumption coordinated management of cabin cooling and battery cooling, ensuring cooling accuracy and system operating efficiency.

[0036] The data acquisition module is used to collect and store thermal management-related data of pure electric vehicles. The thermal management-related data includes heat load source data, temperature data, actuator status data, and cabin temperature setpoint.

[0037] The load calculation module is used to quantify the heat load in real time and predict the heat load for future periods based on the heat management-related data, with the goal of minimizing total energy consumption, and to generate parallel control parameters for the dual evaporators in combination with preset temperature constraints.

[0038] The control execution module is used to send the parallel control parameters of the dual evaporators to the corresponding actuators for execution.

[0039] Furthermore, the following example, using a summer driving thermal management scenario of a pure electric passenger vehicle, will provide a complete explanation of the system's operation:

[0040] In this embodiment, the preset battery pack temperature threshold for the pure electric vehicle is calibrated to 40°C. The default cabin temperature is 26 degrees Celsius when not manually set. The total area of ​​the car windows is 2.5. The cabin's external surface area is 15 The air density is taken as 1.2. The specific heat capacity of air at constant pressure is taken as 1005. The battery internal resistance is calibrated to be 0.001. The compressor's rated efficiency is 0.7. This system communicates in real-time with the vehicle control unit (VCU), battery management system (BMS), vehicle air conditioning control unit, and vehicle display unit via a CAN bus. Various sensors are arranged in preset positions. Actuators include the variable frequency compressor, cabin evaporator electronic expansion valve EXV1, battery evaporator electronic expansion valve EXV2, cabin fan, and battery fan. The driver actively selects to activate this system via the vehicle display unit. If the cabin temperature is not manually set, the system automatically configures the cabin temperature setpoint to 26 degrees Celsius. At the same time, the data acquisition module, load calculation module and control execution module are activated and enter the standby state.

[0041] The data acquisition module collects and stores thermal management-related data in real time through onboard sensors and the vehicle's CAN bus. Under summer outdoor ambient temperatures of 35°C, the module collects real-time data including the number of occupants in the cabin (2) and solar irradiance of 800 ppm. Speed ​​60 Battery charging / discharging current 50A, actual cabin temperature 38°C The average temperature of the battery pack is 42 degrees Celsius. Ambient temperature 35 degrees Celsius Meanwhile, the initial state data of the actuators were collected as follows: compressor speed 0 r / min, opening degree of electronic expansion valves EXV1 and EXV2 both 0%, and speed of cabin fan and battery fan both 0 r / min.

[0042] The load calculation module first calculates the cabin temperature deviation, which is the difference between the actual cabin temperature and the cabin temperature setpoint, i.e., 38℃ - 26℃ = 12. The cabin temperature deviation is greater than ±0.5. The average temperature of the battery pack is 42 degrees Celsius. Temperature greater than the preset battery pack temperature threshold of 40°C Once the triggering conditions are met, the module immediately starts and formally enters the workflow of heat load quantification, future heat load prediction, and optimization of parallel control parameters for the dual evaporators. The load calculation module performs real-time quantitative calculations of the cabin cooling load and battery cooling load according to preset formulas. First, it calculates the fresh air heat exchange volumetric flow rate based on the vehicle speed, which is 0.1 × 60 + 0.5 = 6.5. The value and other collected data are then substituted into the cabin cooling load formula to calculate a cabin cooling load of 2283W. Since the average battery pack temperature of 42℃ is higher than the ambient temperature of 35℃, the relevant data are substituted into the piecewise function formula corresponding to the battery cooling load to calculate a battery cooling load of 6kW. The load calculation module uses a long short-term memory network algorithm to predict the heat load for future periods. The prediction step size is set to 1 minute, with a preset prediction step size of 3. The current cabin cooling load of 2283W, battery cooling load of 6kW, actual cabin temperature of 38℃, battery pack temperature of 43℃, ambient temperature of 35℃, battery charging / discharging current of 50A, and number of passengers of 2 are used as input features. After processing by the long short-term memory network algorithm, the output is a sequence of predicted heat load values ​​for the next 3 minutes, including predicted cabin cooling load and predicted battery cooling load. The load calculation module uses the minimization of total energy consumption as the objective function, combined with cabin temperature constraints. Battery pack temperature constraints Based on the temperature below 40℃ and the collected actuator status data, the optimal control parameters are solved by the model predictive control algorithm. According to the condition judgment result that the cabin temperature deviation exceeds the threshold and the battery pack temperature is preset to the threshold, the load calculation module enters the coordinated cooling mode and outputs the complete parallel control parameters of the dual evaporators. Specifically, the variable frequency compressor speed is 3000 r / min, the electronic expansion valve EXV1 opening is 80%, the electronic expansion valve EXV2 opening is 90%, the cabin fan speed is 1800 r / min, and the battery fan speed is 2000 r / min.

[0043] The control execution module sends the optimized parallel control parameters for the dual evaporators, obtained through the model predictive control algorithm, to the corresponding actuators in real time via the vehicle's CAN bus. The variable frequency compressor, electronic expansion valves EXV1 and EXV2, cabin fan, and battery fan all start and operate stably according to the sent parameters. The dual evaporator branches work synchronously, precisely providing cooling capacity to the cabin and battery pack respectively, achieving a precise match between cooling output and actual heat load demand. After each actuator runs continuously for 10 minutes according to the parallel control parameters for the dual evaporators, the data acquisition module again collects temperature data in real time. The results show an actual cabin temperature of 26℃, a cabin temperature deviation of 0℃ ≤ ±0.5℃, and an average battery pack temperature of 39℃ ≤ the preset battery pack temperature threshold of 40℃, meeting the temperature compliance conditions. The load calculation module then switches from the coordinated cooling mode in the cooling control mode to the temperature maintenance mode. The temperature maintenance mode uses a dual-path independent PID closed-loop control algorithm with a preset adjustment period of 1 second, where the cabin side proportional gain... Integral gain Differential gain Battery-side proportional gain Integral gain Differential gain In this mode, the temperature deviation is first calculated in real time as 0℃, and the cabin temperature deviation is 1℃. Then, the PID algorithm generates the corresponding actuator fine-tuning amount. Since there is no deviation in the cabin temperature, the cabin-side actuator fine-tuning amount is 0. The battery-side fine-tuning amount strictly follows the constraints of electronic expansion valve opening fine-tuning amount ≤ ±2%, fan speed fine-tuning amount ≤ ±50r / min, and total compressor speed fine-tuning amount ≤ ±100r / min. Subsequently, the control execution module continuously sends out the battery-side fine-tuning parameters, so that the electronic expansion valve EXV2 opening is fine-tuned from -1% to +15%, and the battery fan speed is fine-tuned from -20r / min to +150r / min. The other actuators maintain their original parameters. The system then continues to operate at 1-second intervals. The system cycles through temperature data acquisition, temperature deviation calculation, fine-tuning generation, and distribution to maintain a stable cabin temperature of 26℃±0.2℃ and a battery pack temperature of 37℃-40℃. This ensures smooth actuator operation without frequent shocks and maintains a state of minimum total system energy consumption. When the driver manually shuts down the system via the onboard display unit, or when the vehicle is powered off after completing its driving process, the data acquisition module and load calculation module cease all operations. The control execution module then issues a shutdown command to each actuator, which immediately shuts down. The system enters a standby hibernation state, which is only reactivated and puts the system into standby mode when the driver actively turns it on again via the onboard display unit.

[0044] This embodiment describes a scenario of coordinated cooling during summer driving of a pure electric vehicle. When the actual cabin temperature meets the standard but the battery pack temperature exceeds the preset battery pack temperature threshold, the system will enter battery cooling mode, shutting down the cabin evaporator branch and cabin fan, and only controlling the battery-side actuators. When the battery pack temperature meets the standard but the actual cabin temperature exceeds the threshold, the system will enter cabin cooling mode, shutting down the battery evaporator branch and battery fan, and only controlling the cabin-side actuators. The overall workflow of these two modes is consistent with this embodiment, except that the objective function, temperature constraints, and output range of the dual evaporator parallel control parameters are adjusted according to preset rules, which will not be described in detail here.

[0045] The above description is merely a specific embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles, characterized in that, include: The data acquisition module is used to collect and store thermal management-related data of pure electric vehicles. The thermal management-related data includes heat load source data, temperature data, actuator status data and cabin temperature setpoint. The load calculation module is used to quantify the heat load in real time and predict the heat load for future periods based on the heat management-related data, with the goal of minimizing total energy consumption, and to generate parallel control parameters for the dual evaporators in combination with preset temperature constraints. The control execution module is used to send the parallel control parameters of the dual evaporators to the corresponding actuators for execution.

2. The low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 1, characterized in that, The heat load source data includes the number of passengers in the cabin, solar irradiance, vehicle speed, and battery charging and discharging current. The temperature data includes the actual cabin temperature, battery pack temperature, and ambient temperature. The actuator status data includes compressor speed, electronic expansion valve opening, and fan speed.

3. The low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 1, characterized in that, The load calculation module is triggered when there is at least one of the following conditions: cabin temperature deviation > ±0.5℃ and battery pack temperature > preset battery pack temperature threshold. The cabin temperature deviation is the difference between the actual cabin temperature and the cabin temperature setting value.

4. The low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 1, characterized in that, The real-time quantification of heat load is divided into quantified cabin cooling load and battery cooling load. The cabin cooling load is calculated by substituting the number of passengers in the cabin, solar irradiance, vehicle speed, ambient temperature, and actual cabin temperature into the cabin cooling load formula. The battery cooling load is calculated by substituting the battery charging and discharging current, battery pack temperature, and ambient temperature into the battery cooling load formula. The future heat load prediction adopts a long short-term memory network algorithm, using cabin cooling load, battery cooling load, actual cabin temperature, battery pack temperature, ambient temperature, battery charging and discharging current, and number of passengers in the cabin as input features, and outputs cabin cooling load prediction values ​​and battery cooling load prediction values ​​with a preset prediction step size.

5. A low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 4, characterized in that, The parallel control parameters for the dual evaporators are generated by a model predictive control algorithm with the objective function of minimizing total energy consumption, combined with actuator state data, cabin temperature constraints, and battery pack temperature constraints.

6. A low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 3 or 5, characterized in that, The load calculation module divides the refrigeration conditions into cabin refrigeration conditions, battery cooling conditions, and coordinated refrigeration conditions according to the triggering conditions. Each refrigeration condition corresponds to different dual-evaporator parallel control parameters. Under the cabin cooling condition, the battery evaporator branch and battery fan are shut down. The model predictive control algorithm only outputs the parallel control parameters of the dual evaporators, including compressor speed parameters, electronic expansion valve EXV2 opening parameters and cabin fan speed parameters. The objective function removes the battery cooling load prediction term and battery fan term, and the temperature constraint removes the battery pack temperature constraint. Under the battery cooling condition, the cabin evaporator branch and cabin fan are shut down. The model predictive control algorithm only outputs the parallel control parameters of the dual evaporators, including compressor speed parameters, electronic expansion valve EXV1 opening parameters and battery fan speed parameters. The objective function removes the cabin cooling load prediction term and the cabin fan term, and the temperature constraint removes the cabin temperature constraint. The model predictive control algorithm under the coordinated cooling condition outputs complete parallel control parameters for the dual evaporators, including compressor speed parameters, electronic expansion valve EXV1 opening parameters, electronic expansion valve EXV2 opening parameters, cabin fan speed parameters, and battery fan speed parameters.

7. A low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 6, characterized in that, When the parallel control parameters of the dual evaporators are sent to the corresponding actuators and executed, the cabin temperature deviation is ≤ ±0.5℃ under cabin cooling mode, and the battery pack temperature is ≤ preset battery pack temperature threshold under battery cooling mode, or the cabin temperature deviation is ≤ ±0.5℃ and the battery pack temperature is ≤ preset battery pack temperature threshold under synergistic cooling mode, the load calculation module enters the temperature maintenance mode.

8. A low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 7, characterized in that, The temperature maintenance mode adopts a closed-loop dynamic adjustment strategy, which acquires the actual cabin temperature, battery pack temperature and ambient temperature in real time at a preset cycle, and generates actuator fine-tuning data through a PID control algorithm and sends it to the corresponding actuator for fine-tuning.

9. A low-energy-consumption dual-evaporator parallel thermal management system for pure electric vehicles according to claim 1, characterized in that, The system is installed in the vehicle system of a pure electric vehicle and communicates with the vehicle controller (VCU), battery management system (BMS), vehicle air conditioning control unit, and vehicle display unit. It is used for low-energy thermal management of the vehicle cabin cooling and battery cooling during summer driving of pure electric vehicles.

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