Optimal heating control method, control system and vehicle of electric vehicle heat pump system

Abstract

The application discloses an optimal heating control method and system for an electric vehicle heat pump system and a vehicle, and belongs to the technical field of automobile thermal management. The method comprises the following steps: acquiring real-time operation parameters of the heat pump system, and calculating a total heating capacity representing a net contribution of a heat pump compressor to vehicle heating according to the real-time operation parameters; then, taking the total heating capacity as a feedback signal, adopting an extremum seeking control (ESC) algorithm, applying periodic disturbance on an opening degree instruction of a hot gas bypass electronic expansion valve, demodulating gradient information of the total heating capacity relative to the opening degree on line, and driving the opening degree instruction to iteratively optimize in a direction of increasing the total heating capacity until converging to an optimal opening degree. In addition, a safety constraint mechanism with system high pressure, low pressure and exhaust temperature as judgment conditions is integrated. The application can automatically coordinate multiple heat sources such as the heat pump, motor waste heat and PTC, maximize system energy efficiency, and significantly improve the winter range of the electric vehicle.

Description

Technical Field

[0001] This invention relates to the field of automotive thermal management technology, specifically to an optimal heating control method, control system, and vehicle for an electric vehicle heat pump system. Background Technology

[0002] With the increasing popularity of electric vehicles, the issue of reduced driving range in winter has become increasingly prominent, making efficient thermal management systems a key technology for solving this problem. Among these technologies, R290 (propane) heat pumps, due to their excellent environmental characteristics and low-temperature heating performance, are considered an important development direction for next-generation electric vehicle heat pump systems. Under low-temperature heating conditions, how to efficiently utilize the vehicle's limited heat resources to meet the needs of passenger cabin comfort, battery operating temperature, and other aspects, while maximizing system energy efficiency, is currently a hot research topic.

[0003] For example, a prior art electric vehicle thermal management system entitled "Electric Vehicle Thermal Management System Based on Heat Pump Dual Air Conditioning Box and its Control Method" discloses such a system. This system integrates a battery cooling water system, a motor cooling water system, an air conditioning heat pump system, and a PTC heating system, and proposes multiple operating modes. Specifically, in low-temperature driving mode, when the battery needs heating, the system can connect the battery cooling water system and the motor cooling water system in series via a commutation device, utilizing the heat generated during motor operation (i.e., motor waste heat) to heat the battery, while simultaneously heating the passenger compartment. This solution recovers, to a certain extent, the originally lost waste heat, improving the overall vehicle energy utilization rate.

[0004] However, this existing technology still has the following drawbacks in practical applications: 1. The control objective is singular, lacking a global optimization perspective. Existing control strategies typically consider passenger compartment heating and battery heating separately, or switch between them based on simple rules (such as prioritizing battery heating). For example, while the aforementioned existing technologies can utilize waste heat from the motor, their assessment of total heat generation remains at a qualitative level, failing to establish a global mathematical model that can quantitatively assess and uniformly integrate the heat contributions from the passenger compartment, battery, and motor. This prevents the system from accurately determining the true impact of current control actions on the overall vehicle heating performance under dynamically changing operating conditions.

[0005] 2. The control method is passive and cannot achieve online optimization. Existing technologies mostly employ "if...then..." conditional logic or model-based PID control, pre-setting the control parameters or corresponding relationships of each actuator (such as electronic expansion valves, water pumps, etc.). This approach heavily relies on engineers' prior knowledge and offline calibration, making it unsuitable for system aging, component performance drift, and complex and changing environmental conditions. When the system needs to adjust key control variables (such as the opening degree of the hot gas bypass valve) to achieve the optimal ratio of different heat sources (heat pumps, motor waste heat, PTC), existing technologies lack a mechanism that can automatically and online search for and lock the optimal control point during actual system operation.

[0006] 3. Insufficient energy efficiency optimization prevents the full utilization of the advantages of heat pumps. While utilizing waste heat from the motor can reduce the power consumption of a PTC (Power Transmission Control Unit), introducing this waste heat essentially involves sacrificing the motor's own efficiency to obtain heat, resulting in an energy conversion efficiency (equivalent COP) that is typically lower than that of a high-efficiency heat pump. Current technologies, when controlling heating, fail to effectively distinguish between the heat absorbed by the heat pump from the environment and the heat generated by the reduced motor efficiency. In pursuing total heating capacity, this may inappropriately encourage the use of inefficient motors for heating, thereby inhibiting the exploitation of the heat pump system's own high-efficiency heating potential and failing to achieve true global energy efficiency optimization.

[0007] Therefore, how to establish a reasonable global heating performance evaluation system in the complex vehicle thermal management system, and design an intelligent control method that can adaptively and online coordinate multiple heat sources such as heat pumps, motor waste heat and PTC, and actively pursue the maximization of global energy efficiency, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] The present invention aims to solve at least one technical problem existing in the above-mentioned background art, and to provide an optimal heating control method, control system and vehicle for electric vehicle heat pump system.

[0009] The technical solution of this invention is: an optimal heating control method for an electric vehicle heat pump system, comprising: Obtain the real-time operating parameters of the heat pump system, and calculate the total global heating capacity to characterize the work contribution of the compressor based on the real-time operating parameters; Using the total global heat capacity as feedback signal, the extreme value search control (ESC) algorithm is adopted to automatically adjust the opening command of the hot gas bypass electronic expansion valve online. The extreme value search control (ESC) algorithm applies periodic perturbations to the opening command, demodulates and extracts the gradient information of the total global heat production relative to the opening command, and drives the opening command to move in the direction that increases the total global heat production according to the gradient information, until it converges to the optimal opening that maximizes the total global heat production.

[0010] According to the present invention, an optimal heating control method for an electric vehicle heat pump system is provided, wherein the method for calculating the total global heating capacity includes: Calculate the heat output of the crew cabin; Determine if the battery has a heating request. If so, calculate the battery's heating capacity. If not, set the battery's heating capacity to 0. Obtain the reduced heating power of the motor and the actual heating power of the PTC heater; The total global heating capacity is calculated using the following formula: in: -Total global heat output; -Heating capacity of the crew cabin; -Battery heating; - Reduced heating power of the motor; - The actual heating power of the PTC heater.

[0011] According to the optimal heating control method for an electric vehicle heat pump system provided by the present invention, the heating capacity of the passenger compartment is calculated by the following formula: in: -Air-side mass flow rate; -Specific heat capacity of air; - Passenger cabin exhaust temperature; - Passenger cabin air intake temperature; The air inlet temperature Calculated using the formula for estimating the ratio of internal and external circulation: in: - Ratio of internal and external circulation; -Interior temperature; - Ambient temperature.

[0012] According to the optimal heating control method for an electric vehicle heat pump system provided by the present invention, the battery heating capacity is calculated by the following formula: in, - Mass flow rate of coolant flowing through the battery pack; -Specific heat capacity of coolant; - Battery pack coolant outlet temperature; - Coolant inlet temperature of the battery pack.

[0013] According to the optimal heating control method for an electric vehicle heat pump system provided by the present invention, the extreme value search control (ESC) algorithm performs the following iterative steps in each control cycle: A sinusoidal disturbance is superimposed on the base opening to generate control commands. in: -time t Opening control commands; - t The basic opening at any given time; - The amplitude of the sinusoidal disturbance signal; - The angular frequency of the sinusoidal disturbance signal; Obtain the total global heat output for the current cycle; The total global heat output for the current cycle is sequentially subjected to high-pass filtering, demodulation with the sinusoidal perturbation, and low-pass filtering to obtain the gradient estimate. The base opening is updated based on the gradient estimate, i.e. in: - Updated base opening; - Base opening size before update; -Integral gain; - Gradient estimate; - Control cycle or integral step size; The final control command is limited to the preset minimum and maximum opening degrees.

[0014] An optimal heating control method for an electric vehicle heat pump system provided by the present invention further includes a safety constraint integration mechanism, wherein the safety constraint integration mechanism performs the following steps: At the beginning of each control cycle, monitor the high pressure, exhaust temperature, and low pressure of the heat pump system; If the high pressure is greater than or equal to the high pressure threshold, or the exhaust temperature is greater than or equal to the exhaust temperature threshold, or the low pressure is less than or equal to the low pressure threshold, the extreme value search control ESC algorithm is immediately suspended, and the safety protection module takes over to perform the corresponding protection action.

[0015] The present invention provides an optimal heating control method for an electric vehicle heat pump system. When the high-pressure protection is triggered, the protection action includes increasing the opening of the hot gas bypass electronic expansion valve and / or reducing the compressor speed; When the exhaust temperature protection is triggered, the protection action includes increasing the opening of the hot gas bypass electronic expansion valve; When the low-pressure protection is triggered, the protection action includes increasing the opening of the hot gas bypass electronic expansion valve and / or reducing the compressor speed.

[0016] This invention also relates to an optimal heating control system for an electric vehicle heat pump system, comprising: A heat pump system, the system comprising at least a compressor, an in-vehicle heat exchanger, an external heat exchanger, a hot gas bypass electronic expansion valve, and a heat distribution circuit for providing heat to the passenger compartment and / or the battery; The sensor unit is used to collect real-time operating parameters of the heat pump system; A controller, electrically connected to the sensor unit and the hot gas bypass electronic expansion valve, is configured to execute the optimal heating control method of the electric vehicle heat pump system described above.

[0017] According to an optimal heating control system for an electric vehicle heat pump system provided by the present invention, the sensor unit includes: Multiple temperature sensors are used to measure the temperature of the passenger compartment exhaust air, the interior temperature, and the ambient temperature. Multiple temperature sensors are used to measure the inlet and outlet temperatures of the battery pack coolant. Pressure sensors and temperature sensors used to measure the high pressure, low pressure, and compressor exhaust temperature of the system; And a vehicle CAN bus interface for acquiring signals such as blower speed, internal / external circulation ratio, coolant flow rate, and motor derating power.

[0018] The present invention also relates to a vehicle comprising an optimal heating control system for the aforementioned electric vehicle heat pump system.

[0019] The advantages of this invention are: 1. A unique global heating capacity precision assessment system that overcomes the limitations of traditional local optimization. This solution is the first to integrate the passenger compartment heating capacity, battery heating capacity, reduced motor heating power, and PTC heater power into a unified calculation framework. Unlike traditional methods that focus solely on a single user (such as passenger compartment outlet air temperature), this invention calculates a global total heating capacity representing the compressor's work contribution by weighted integration of contributions from multiple heat sources and users, and uses this as the global optimization target. This evaluation system accurately reflects the total contribution of the heat pump system to the vehicle's heating capacity, providing a precise and comprehensive basis for subsequent global optimal control.

[0020] 2. Optimization objectives with clear physical meaning drive the system to achieve optimal global energy efficiency from the root cause. The core insight of this solution lies in the definition of the optimization objective. By subtracting the reduced heating power of the motor and the power of the PTC heater from the total heating capacity, the total global heating capacity generated by the compressor is deduced, and this is used as the maximization target of the ESC algorithm. This design has a clear physical meaning: guiding the control system to prioritize and maximize the utilization of the heat pump system's own high-efficiency heating capacity while meeting the total heat demand, naturally reducing reliance on inefficient PTC electric heating. This is not a simple switching logic, but a dynamic optimal strategy that pursues the energy efficiency limit at the system level.

[0021] 3. Powerful model-free adaptive capability, completely eliminating tedious bench calibration work. This solution employs the Extreme Value Search Control (ESC) algorithm, a real-time optimization technique that does not rely on a precise mathematical model of the controlled object. It automatically searches for the operating point that maximizes the objective function by applying and demodulating disturbance signals. This means that this solution eliminates the need for extensive bench calibrations to build complex control maps under different operating conditions during the development phase. More importantly, the ESC algorithm can compensate online for system characteristic drift caused by system aging, component wear, and minor refrigerant leaks, maintaining optimal heating performance throughout the entire vehicle lifecycle and exhibiting extremely strong robustness.

[0022] 4. An intelligent security and performance priority nesting mechanism achieves a perfect balance between performance mining and system protection. This solution employs a safety constraint integration mechanism to construct an intelligent control architecture that prioritizes safety over performance. Under normal conditions, the ESC algorithm can freely explore the boundaries of heating performance. Once potential risks such as excessively high pressure, excessively high exhaust temperature, or excessively low pressure are detected, the safety protection module immediately takes over with the highest priority, proactively suspending ESC optimization and executing explicit protective actions, such as increasing the bypass valve opening or reducing the compressor speed under specific risk conditions. This robust safety net allows the ESC algorithm to more boldly push the system into its high-efficiency zone, achieving the ultimate exploitation of heating performance.

[0023] 5. The pure software implementation offers excellent functionality portability and cost advantages. This invention is essentially an advanced control algorithm. Its innovation lies in its control logic and data fusion method, which can be fully integrated into existing vehicle controllers (such as thermal management domain controllers) as software code. The required input signals all come from existing vehicle sensors and the CAN bus, requiring no additional hardware costs. Therefore, this solution offers extremely high cost-effectiveness and cross-platform portability, and can be quickly deployed as a core functional software on different vehicle platforms, demonstrating significant commercial value. Attached Figure Description

[0024] Figure 1 This is a flowchart of the optimal heating control method for the electric vehicle heat pump system of the present invention. Detailed Implementation

[0025] Embodiments of the present invention are described in detail below, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0026] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0027] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the scope of protection of the invention.

[0029] This invention proposes an optimal heating control method and system for electric vehicle heat pump systems, particularly suitable for heat pump systems using R290 (propane) as a refrigerant. It constructs a global heating capacity index that can accurately assess the actual heating contribution of the heat pump compressor. Then, using this index as the optimization target, an Extreme Value Search Control (ESC) algorithm is employed to automatically search online for the optimal opening degree of the hot gas bypass electronic expansion valve that maximizes this index. Simultaneously, a safety constraint integration mechanism is set up to ensure that the entire optimization process is conducted within the system's safety boundaries.

[0030] Example 1: This example illustrates the control system architecture and basic working principle for implementing the method of the present invention.

[0031] The optimal heating control system for electric vehicle heat pump system of the present invention mainly includes: heat pump system, sensor unit and controller.

[0032] The heat pump system includes at least a compressor, an in-vehicle heat exchanger, an external heat exchanger, a main electronic expansion valve, a hot gas bypass electronic expansion valve, and a heat distribution circuit for providing heat to the passenger compartment and / or the battery. The hot gas bypass electronic expansion valve is the core controlled object of this invention; its opening degree directly affects the compressor's exhaust bypass volume, and thus the system's heating capacity and energy efficiency.

[0033] The sensor unit is used to collect real-time operating parameters of the heat pump system. Specifically, the sensor unit includes: multiple temperature sensors for measuring the passenger compartment outlet air temperature, vehicle interior temperature, and ambient temperature; multiple temperature sensors for measuring the battery pack coolant inlet and outlet temperatures; pressure and temperature sensors for measuring the system's high-pressure, low-pressure, and compressor exhaust temperatures; and an interface module for acquiring blower speed, internal / external circulation ratio, coolant flow rate, and motor derating power signals from the vehicle's CAN bus.

[0034] The controller is electrically connected to the aforementioned sensor unit and the hot gas bypass electronic expansion valve. The controller is configured to execute the optimal heating control method proposed in this invention, and internally includes a target quantity calculation module, an extreme value search control (ESC) module, and a safety protection module.

[0035] like Figure 1 As shown, the control method in this embodiment includes the following steps: Step S11: Obtain the real-time operating parameters of the heat pump system, and calculate a global total heating capacity representing the effective heating output driven by the work done by the heat pump compressor based on the real-time operating parameters. Step S12: Using the total global heat output as a feedback signal, the extreme value search control (ESC) algorithm is adopted to automatically adjust the opening command of the hot gas bypass electronic expansion valve online.

[0036] In this embodiment, the ESC algorithm applies a small-amplitude periodic perturbation to the opening command of the hot gas bypass electronic expansion valve. By analyzing the response of the total global heating capacity to this perturbation, the algorithm demodulates and extracts the gradient information of the total global heating capacity relative to the opening command in real time. Subsequently, the algorithm drives the opening command to move in the direction that increases the total global heating capacity based on this gradient information, that is, iteratively optimizes along the direction of gradient increase until it finally converges to the optimal opening degree that maximizes the total global heating capacity.

[0037] The control system and its basic principles constructed in this embodiment have significant advantages over traditional control methods based on lookup tables or PID control. First, the control objective is defined as a global total heat output with clear physical meaning, freeing optimization from being limited to a single local parameter. Second, the model-free ESC algorithm employed allows the system to be put into operation without any prior bench calibration and to automatically adapt to changes in system characteristics throughout its lifecycle.

[0038] Example 2: This example illustrates how to calculate the total global heat output as a control target to accurately reflect the contribution of the compressor's work.

[0039] In this embodiment, the target quantity calculation module of the controller performs the following steps: Step S21: Calculate the heat output of the crew cabin ; Step S22: Determine if the battery has a heating request. If so, calculate the battery's heating capacity. If not, then order =0; Step S23: Obtain the motor's reduced heating power via CAN bus and the actual heating power of the PTC heater The motor's reduced heating power This refers to the power at which the motor control system actively instructs the motor to operate at a non-peak efficiency point to convert the extra electrical energy consumed into heat energy for heating, in order to meet the vehicle's heating requirements. This power value can be transmitted by the motor controller via the CAN bus. Step S24: Calculate the total global heating capacity according to the following formula. : and The sum represents the total effective heating benefit provided by the thermal management system to the crew compartment and battery, which is contributed by three heat sources: the heat pump compressor, the motor, and the PTC heater. To accurately extract the value of the specific heat source generated by the compressor's work, the contributions of the other known heat sources must be subtracted from the total benefit. and Based on the law of conservation of energy, the calculation results are... This accurately represents the net heating capacity generated by the compressor. Maximizing this amount is equivalent to guiding the system to prioritize and maximize the utilization of the heat pump's own high-efficiency heating capacity.

[0040] Among them, the crew cabin heating Precise calculations based on air-side parameters: The air-side mass flow rate can be estimated from the blower speed setting; This refers to the air outlet temperature in the crew cabin. This refers to the passenger compartment air intake temperature. Calculated using the formula for estimating the ratio of internal and external circulation: in The ratio of internal to external circulation. For the temperature inside the car, The ambient temperature is used for estimation. This method is simple and efficient, utilizing existing sensors without additional cost.

[0041] Battery heating Precise calculations based on coolant-side parameters: in The mass flow rate of the coolant flowing through the battery pack is determined by the coolant pump duty cycle and loop characteristics. and These are the outlet and inlet temperatures of the battery pack coolant, respectively.

[0042] This embodiment establishes a unique global heating capacity accuracy assessment system. For the first time, it integrates the heat demand from the passenger compartment and battery (two user-side components) with the contributions from the motor and PTC (two auxiliary heat sources), breaking away from the limitations of traditional methods that can only estimate the heating capacity of a single user side (such as the passenger compartment). This allows subsequent ESC (Energy Management System) optimization to be performed from a complete and realistic global perspective, ensuring the global optimality of the optimization decision.

[0043] Example 3: This example is used to elaborate and supplement the internal iterative steps of the ESC algorithm in detail.

[0044] The ESC module in each control cycle Δt The following iterative steps are performed internally: Step S31: At the basic opening A sinusoidal disturbance is superimposed on the control command to generate the actual output. The formula is: in, a It is the amplitude of the sinusoidal disturbance signal, and its selection principle is: to keep it as small as possible to maintain system stability, provided that it can cause a identifiable output response (such as the change in the total global heat generation exceeding the measurement noise level). ω It is the angular frequency of the sinusoidal disturbance signal. The selection principle is: less than the cutoff frequency of the controlled object (hot gas bypass circuit), but higher than the frequency of the main disturbance of the system, so as to ensure the accuracy of gradient extraction.

[0045] Step S32: Obtain the total global heat output for the current cycle calculated using the method of Example 2. .

[0046] Step S33: Analyze the global total heat output signal. Signal processing is performed to extract gradient information. The process is as follows: a. High-pass filtering: filters out The DC component and low-frequency drift are removed, and only the dynamic response component with the same frequency as the disturbance is retained to obtain the signal. .

[0047] b. Demodulation: Demodulating the high-pass filtered signal Sinusoidal perturbation signal of the same frequency Multiplication is performed to achieve coherent demodulation and obtain the signal. .

[0048] c. Low-pass filtering: for demodulated signals Low-pass filtering is performed to remove high-frequency harmonic components, ultimately yielding a gradient estimate that reflects the gradient direction and magnitude. g(t) .like g(t) >0 indicates that the current opening degree is less than the optimal opening degree; if g(t) If <0, then the opposite is true.

[0049] Step S34: Update the basic opening based on the gradient estimate g(t). The formula is: in, This is the updated base opening. This is the base opening size before the update; The integral gain determines the convergence speed of the optimization; This is the gradient estimate; To control the cycle or integral step size.

[0050] This step is essentially an integral controller that drives the base opening to move in the direction of gradient increase (i.e., the direction of increasing total global heat output).

[0051] Step S35: Send the final control command Limiting the opening to a preset minimum (e.g., 5%) and maximum (e.g., 100%) ensures the normal operation of the physical actuator; Step S36: Determine if ESC has converged. If the gradient estimate is... g(t) The absolute value is consistently less than a preset small threshold (e.g. If the disturbance duration exceeds the preset number of cycles, the system is considered to be close to or at its maximum heating capacity, and the disturbance amplitude can be appropriately reduced or the current state can be maintained; otherwise, continue to seek optimization.

[0052] The ESC algorithm in this embodiment is equivalent to a model-free, real-time gradient ascent optimizer. By applying a probe signal to the control input and measuring the gradient of the objective function through coherent demodulation at the output, the algorithm can perceive the direction of the optimal point without relying on any system model. Its greatest advantage lies in its powerful model-free adaptive capability, completely eliminating the need for complex calibration during the development phase, and being able to combat system aging and parameter drift online, achieving optimal performance throughout its entire lifecycle.

[0053] Example 4: This example illustrates how to ensure the safety of the ESC optimization process.

[0054] This system integrates a safety constraint integration mechanism with the highest priority, employing a three-level nested architecture where safety protection logic (highest priority) is higher than ESC optimization logic (normal priority), which is higher than the PID execution module. Specifically, at the beginning of each control cycle, the controller prioritizes executing the safety monitoring process: First, monitor the high pressure, exhaust temperature, and low pressure of the heat pump system.

[0055] Then, threshold judgment is performed. If the high pressure is greater than or equal to the high pressure threshold (e.g., 2.3~3.0 MPa, the specific value is determined according to the system's pressure resistance level, a typical example is 2.3 MPa), or the exhaust temperature is greater than or equal to the exhaust temperature threshold (e.g., 110~130℃, a typical example is 110℃), or the low pressure is less than or equal to the low pressure threshold (e.g., 0.1~0.15 MPa), then safety protection is triggered.

[0056] Once triggered, the security mechanism will immediately perform two actions: First, it will suspend the optimization process of the ESC algorithm and freeze the base opening for its integral updates. Second, activate the safety protection module, which takes over the control of the hot gas bypass electronic expansion valve and / or compressor, and performs specific protective actions.

[0057] The design of protective actions directly corresponds to the sources of risk: 1. When the high-pressure protection is triggered, the risk is that the system pressure is too high and needs to be unloaded. Therefore, the actions include: increasing the opening of the hot gas bypass electronic expansion valve (to provide an exhaust pressure relief passage) and / or reducing the compressor speed.

[0058] 2. When the exhaust temperature protection is triggered, the risk is that the compressor outlet temperature is too high and needs to be cooled. Therefore, the actions include: increasing the opening of the hot gas bypass electronic expansion valve (to directly bypass some of the high-temperature exhaust to the low-pressure side, increasing the intake volume to cool the compressor).

[0059] 3. When the low-pressure protection is triggered, the risk is that the system low pressure is too low. It is necessary to prevent the low-pressure side from continuing to drop in pressure. Therefore, the actions include: increasing the opening of the hot gas bypass electronic expansion valve (to increase the low pressure) and / or reducing the compressor speed.

[0060] This embodiment designs an intelligent nested priority mechanism that prioritizes safety over performance. Within the safety boundary, the ESC algorithm can explore and exploit the limits of heating performance without constraints; once the safety boundary is reached, the solidified safety protection logic immediately gains the highest priority, pulling the system back to the safe zone. This enhances robustness and achieves a balance between performance exploitation and system protection.

[0061] like Figure 1 As shown, the optimal heating control method for the electric vehicle heat pump system of the present invention is as follows: Phase 1: Initialization and Signal Acquisition After the system is powered on, the controller completes initialization and presets the parameters of the ESC algorithm. a , ω , , Δt ), and the basic opening Set a safe initial value. After entering the main loop, execute the following steps in each control cycle: (1) Obtain all real-time operating parameters through the sensor unit and CAN bus interface: , , , , , , , , , , and high voltage HP Exhaust temperature Low pressure LP .

[0062] Phase Two: Security Status Detection and Highest Priority Decision (2) Determine whether any of the security trigger conditions are met: HP ≥ or ≥ or LP ≤ .

[0063] (3) If so, immediately implement the following security protection procedures: (3a) Set the working status flag of the ESC module to pause and freeze the integration terms. Update.

[0064] (3b) Based on the specific triggering conditions, the safety protection module calculates and executes the corresponding protection action (such as increasing the bypass valve opening) and outputs the final opening command. And skip all the following optimization steps and return directly to (1) to enter the next cycle.

[0065] (4) If not, the system is in a safe state, the working status of the ESC module is marked as running, and the subsequent optimization steps continue to be executed.

[0066] Phase 3: Calculation of Global Optimization Objective (5) Calculate the heat output of the crew cabin: .

[0067] (6) Determine the battery heating request; if so, calculate... If not =0.

[0068] (7) Calculate the total global heat output representing the contribution of the compressor. : .

[0069] Phase Four: ESC Optimization Iteration and Instruction Output (8) The current total global heat output The sample is fed into a high-pass filter to obtain... .

[0070] (9) Demodulation: .

[0071] (10) The sample is fed into a low-pass filter to obtain the gradient estimate. g .

[0072] (11) Update the base opening: .

[0073] (12) Add the perturbation to the updated base opening to generate the temporary opening instruction for the current period: .

[0074] (13) Restrict temporary opening commands to the allowed physical range. , To prevent the actuator from malfunctioning or the system from entering a dangerous area due to out-of-range commands, the opening command after being limited is used as the final control command. It is then sent to the PID controller for execution.

[0075] Phase 5: Instruction Execution and Loops (14) Final opening command Send to the hot gas bypass electronic expansion valve for execution.

[0076] (15) Wait for the next control cycle Δt Return to step (1).

[0077] Through the aforementioned cyclically executed control process, this invention integrates online optimization of the global optimal target with robust system safety protection, achieving the ultimate and safe exploitation of the heating performance of electric vehicle heat pump systems.

[0078] In addition, the present invention also provides a vehicle that integrates the aforementioned advanced control functions.

[0079] A vehicle includes an optimal heating control system for an electric vehicle heat pump system as described in the above embodiments. The vehicle's thermal management domain controller operates all the control methods provided by this invention, and can automatically adjust the heat pump system to its optimal energy-efficiency operating point based on real-time vehicle conditions, environmental conditions, and passenger needs.

[0080] For users of this vehicle, the advantages of this invention are: in low-temperature environments, the vehicle's heating response is faster, and battery power consumption is lower, thus directly improving the actual driving range in winter. For vehicle manufacturers, this purely software-based solution significantly improves the vehicle's core energy efficiency indicators without increasing hardware costs, possessing extremely high commercial value and cross-platform deployment flexibility.

[0081] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An optimal heating control method for an electric vehicle heat pump system, characterized in that, include: Obtain the real-time operating parameters of the heat pump system, and calculate the total global heating capacity to characterize the work contribution of the compressor based on the real-time operating parameters; Using the total global heat capacity as feedback signal, the extreme value search control (ESC) algorithm is adopted to automatically adjust the opening command of the hot gas bypass electronic expansion valve online. The extreme value search control (ESC) algorithm applies periodic perturbations to the opening command, demodulates and extracts the gradient information of the total global heat production relative to the opening command, and drives the opening command to move in the direction that increases the total global heat production according to the gradient information, until it converges to the optimal opening that maximizes the total global heat production.

2. The method according to claim 1, characterized in that, The method for calculating the total global heat output includes: Calculate the heat output of the crew cabin; Determine if the battery has a heating request. If so, calculate the battery's heating capacity. If not, set the battery's heating capacity to 0. Obtain the reduced heating power of the motor and the actual heating power of the PTC heater; The total global heating capacity is calculated using the following formula: in: -Total global heat output; -Heating capacity of the crew cabin; -Battery heating; - Reduced heating power of the motor; - The actual heating power of the PTC heater.

3. The method according to claim 2, characterized in that, The heat output of the crew compartment is calculated using the following formula: in: -Air-side mass flow rate; -Specific heat capacity of air; - Passenger cabin exhaust temperature; - Passenger cabin air intake temperature; The air inlet temperature Calculated using the formula for estimating the ratio of internal and external circulation: in: - Ratio of internal and external circulation; -Interior temperature; - Ambient temperature.

4. The method according to claim 2, characterized in that, The battery's heat output is calculated using the following formula: in, - Mass flow rate of coolant flowing through the battery pack; -Specific heat capacity of coolant; - Battery pack coolant outlet temperature; - Coolant inlet temperature of the battery pack.

5. The method according to claim 1, characterized in that, The extreme value search control (ESC) algorithm performs the following iterative steps in each control cycle: A sinusoidal disturbance is superimposed on the base opening to generate control commands. in: -time t Opening control commands; - t The basic opening at any given time; - The amplitude of the sinusoidal disturbance signal; - The angular frequency of the sinusoidal disturbance signal; Obtain the total global heat output for the current cycle; The total global heat output for the current cycle is sequentially subjected to high-pass filtering, demodulation with the sinusoidal perturbation, and low-pass filtering to obtain the gradient estimate. The base opening is updated based on the gradient estimate, i.e. in: - Updated base opening; - Base opening size before update; -Integral gain; - Gradient estimate; - Control cycle or integral step size; The final control command is limited to the preset minimum and maximum opening degrees.

6. The method according to claim 1, characterized in that, It also includes a security constraint integration mechanism that performs the following steps: At the beginning of each control cycle, monitor the high pressure, exhaust temperature, and low pressure of the heat pump system; If the high pressure is greater than or equal to the high pressure threshold, or the exhaust temperature is greater than or equal to the exhaust temperature threshold, or the low pressure is less than or equal to the low pressure threshold, the extreme value search control ESC algorithm is immediately suspended, and the safety protection module takes over to perform the corresponding protection action.

7. The method according to claim 6, characterized in that, When the high-pressure protection is triggered, the protection action includes increasing the opening of the hot gas bypass electronic expansion valve and / or reducing the compressor speed; When the exhaust temperature protection is triggered, the protection action includes increasing the opening of the hot gas bypass electronic expansion valve; When the low-pressure protection is triggered, the protection action includes increasing the opening of the hot gas bypass electronic expansion valve and / or reducing the compressor speed.

8. An optimal heating control system for an electric vehicle heat pump system, characterized in that, include: A heat pump system, the system comprising at least a compressor, an in-vehicle heat exchanger, an external heat exchanger, a hot gas bypass electronic expansion valve, and a heat distribution circuit for providing heat to the passenger compartment and / or the battery; The sensor unit is used to collect real-time operating parameters of the heat pump system; A controller electrically connected to the sensor unit and the hot gas bypass electronic expansion valve, and configured to perform the optimal heating control method for the electric vehicle heat pump system according to any one of claims 1 to 7.

9. The system according to claim 8, characterized in that, The sensor unit includes: Multiple temperature sensors are used to measure the temperature of the passenger compartment exhaust air, the interior temperature, and the ambient temperature. Multiple temperature sensors are used to measure the inlet and outlet temperatures of the battery pack coolant. Pressure sensors and temperature sensors used to measure the high pressure, low pressure, and compressor exhaust temperature of the system; And a vehicle CAN bus interface for acquiring signals such as blower speed, internal / external circulation ratio, coolant flow rate, and motor derating power.

10. A vehicle, characterized in that, The optimal heating control system for an electric vehicle heat pump system as described in claim 8 or 9.

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