A method and device for calibrating and optimizing a new energy vehicle thermal management system and a medium

Abstract

This application discloses a calibration and optimization method, device, and medium for a thermal management system of a new energy vehicle. Relating to the field of thermal management, it addresses the problems of scattered engineering data, difficulty in identifying complex operating conditions, multi-objective conflicts, and high safety risks in existing calibration methods. First, it acquires and standardizes various engineering data generated during the operation of the new energy vehicle's thermal management system, forming unified engineering objects and establishing relationships between these objects. This association of scattered engineering data improves data utilization efficiency. Based on vehicle operation logs, it identifies the current thermal management operating condition and determines candidate calibration objects. By analyzing the control processes corresponding to the candidate calibration objects, it locates problem nodes and generates at least one candidate calibration scheme based on these nodes. Safety constraint verification and arbitration are then performed on the generated candidate calibration schemes. Finally, it generates corresponding verification schemes based on the target calibration scheme for closed-loop feedback, improving the calibration efficiency and reliability of the thermal management system.

Description

Technical Field

[0001] This application relates to the field of thermal management, and in particular to a calibration and optimization method, apparatus and medium for a thermal management system of a new energy vehicle. Background Technology

[0002] With the rapid development of new energy vehicles, thermal management systems have evolved from traditional passenger compartment air conditioning to comprehensive control systems integrating passenger compartment comfort control, battery thermal management, electric drive system thermal management, and vehicle energy consumption management. New energy vehicle thermal management systems typically include an electric compressor, electronic expansion valve (EXV), electric fan, water pump, positive temperature coefficient heater (PTC heater), and various temperature and pressure sensors. These are coordinated and controlled by the vehicle control unit (VCU), battery management system (BMS), and thermal management controller to meet the cooling, heating, and safety protection requirements under different operating conditions. Because the calibration parameters differ for different vehicle models, thermal management topologies, and control strategies, repeated debugging and optimization of numerous calibration parameters are necessary for various operating conditions.

[0003] Currently, the calibration of thermal management systems for new energy vehicles mainly relies on calibration engineers analyzing the current operating conditions using requirements documents, communication matrices, data dictionaries, calibration parameter files, and real vehicle or bench test logs. Based on the test results, they adjust calibration parameters such as compressor speed, electronic expansion valve opening, electronic fan duty cycle, protection thresholds, and control parameters, and then verify the calibration effect through multiple rounds of testing. Although auxiliary software such as log analysis tools, calibration management tools, and automated testing tools exist, most of these tools can only perform single functions such as data analysis, parameter modification, or test execution. They lack unified analysis of the relationships between different engineering data, making it difficult to form a complete calibration process covering operating condition identification, parameter recommendation, safety verification, and test validation.

[0004] Therefore, the following problems exist: On the one hand, the calibration of the thermal management system involves multi-source engineering data such as requirements documents, communication data, calibration parameters, and operation logs. There is a lack of a unified correlation analysis mechanism between various types of data, which makes the calibration process heavily reliant on manual experience and results in low calibration efficiency. On the other hand, for complex operating conditions such as dual-cooling, forced control debugging, and electronic expansion valve reset, the existing calibration methods are unable to automatically generate calibration suggestions based on the actual operating status and lack a safety constraint verification and validation mechanism for candidate calibration parameters, which affects calibration efficiency and consistency.

[0005] Therefore, providing a solution to improve the calibration efficiency and reliability of the thermal management system of new energy vehicles is a technical problem that urgently needs to be solved by those in the field. Summary of the Invention

[0006] The purpose of this application is to provide a calibration and optimization method, device and medium for a thermal management system of a new energy vehicle, so as to solve the problems of low calibration efficiency and low calibration reliability of the thermal management system.

[0007] To address the aforementioned technical problems, this application provides a calibration and optimization method for a thermal management system of a new energy vehicle, comprising: Acquire engineering data for the thermal management system of new energy vehicles, wherein the engineering data includes at least demand data, communication data, calibration parameter data, and vehicle operation logs; The engineering data is standardized to generate multiple engineering objects, and the relationships between the multiple engineering objects are established. The current thermal management condition is identified based on the vehicle operation log, and candidate calibration objects corresponding to the current thermal management condition are determined based on the correlation. Based on the analysis of the candidate calibration objects, the control process corresponding to the current thermal management condition is determined, and the problem nodes affecting the current thermal management condition are identified. At least one candidate calibration scheme is generated based on the problem node; The candidate calibration schemes are subjected to security constraint verification. Arbitrate the candidate calibration schemes that have passed the safety constraint verification based on multiple thermal management control objectives to obtain the target calibration scheme; A verification scheme is generated based on the target calibration scheme, and the relationships between the engineering objects and the corresponding calibration parameter versions are updated based on the verification results.

[0008] Optionally, the plurality of engineering objects include demand objects, operating condition objects, signal objects, actuator objects, calibration objects, protection objects, and test objects; Establishing relationships between multiple project objects includes: Establish the requirement association between requirement objects and operating condition objects, the operating condition identification association between operating condition objects and signal objects, the control association between actuator objects and signal objects, the calibration association between actuator objects and calibration objects, the constraint association between protection objects and actuator objects and calibration objects, and the verification association between test objects and operating condition objects and calibration objects.

[0009] Optionally, determining the candidate calibration object corresponding to the current thermal management condition based on the association relationship includes: Match the corresponding operating condition object according to the current thermal management operating condition; The actuator object participating in the current thermal management condition control is determined based on the signal object associated with the operating condition object; Candidate calibration objects are determined based on the calibration object associated with the actuator object; The adjustment constraints corresponding to the candidate calibration object are determined based on the protection object associated with the candidate calibration object.

[0010] Optionally, the step of analyzing the control process corresponding to the current thermal management condition based on the candidate calibration object and determining the problem nodes affecting the current thermal management condition includes: Obtain control request signals from the vehicle operation log and determine whether a current thermal management requirement has been generated; Obtain the corresponding control permission signal and determine whether the control request meets the execution conditions; Acquire actuator control commands and actuator feedback status, and determine whether the actuator responds to the corresponding control commands; Acquire the temperature, pressure, or flow signals corresponding to the actuator, and determine whether the execution result meets the control requirements corresponding to the current thermal management condition; Obtain protection status information and determine whether the control process is limited by protection conditions; Based on the correlation between control requests, control permissions, actuator responses, execution feedback, and protection status, identify the problem nodes affecting the current thermal management conditions.

[0011] Optionally, generating at least one candidate calibration scheme based on the problem node includes: Identify candidate calibration objects associated with the problem node; The adjustment direction of the candidate calibration object is determined based on the difference between the running state and the target state corresponding to the problem node; Retrieve historical calibration results from the historical calibration records that correspond to the current thermal management conditions; Based on the historical calibration results, determine the candidate adjustment amount corresponding to each candidate calibration object; At least one candidate calibration scheme is generated based on multiple candidate calibration objects and their corresponding candidate adjustment values.

[0012] Optionally, the step of performing safety constraint verification on the candidate calibration scheme and arbitrating based on multiple thermal management control objectives includes: Obtain the corresponding security constraints based on the protected object associated with the candidate calibration scheme; The candidate calibration schemes are verified according to the aforementioned security constraints, and candidate calibration schemes that do not meet the security constraints are filtered out. The impact of the remaining candidate calibration schemes on passenger compartment thermal management requirements, battery thermal management requirements, vehicle energy consumption, and system safety was determined. When multiple thermal management requirements can be met simultaneously, the target calibration scheme is determined based on the degree of completion of the requirements corresponding to each candidate calibration scheme. When multiple thermal management requirements cannot be met simultaneously, the target calibration scheme is determined based on the priority of each thermal management requirement under the current operating conditions.

[0013] Optionally, the step of generating a verification scheme based on the target calibration scheme and updating the association between the engineering objects and the corresponding calibration parameter versions based on the verification results includes: The associated actuator objects, operating condition objects, and test objects are determined based on the target calibration scheme; Generate corresponding verification conditions, test steps, and verification criteria based on the described working conditions and test objects; The verification results were obtained by executing the verification scheme. When the verification results meet the verification conditions, the calibration parameters corresponding to the target calibration scheme are determined as the valid version, and the target calibration scheme, applicable working conditions and verification results are associated. When the verification result does not meet the verification conditions, the verification result is associated with the corresponding calibration parameter version for subsequent calibration optimization.

[0014] To address the aforementioned technical problems, this application also provides a calibration and optimization device for a thermal management system of a new energy vehicle, comprising: The data processing module is used to acquire engineering data of the thermal management system of new energy vehicles. The engineering data includes at least demand data, communication data, calibration parameter data, and vehicle operation logs. The working condition analysis module is used to standardize the engineering data, generate multiple engineering objects, and establish the relationship between the multiple engineering objects. The operating condition identification module is used to identify the current thermal management operating condition based on the vehicle operation log, and to determine the candidate calibration object corresponding to the current thermal management operating condition based on the association relationship; The control process analysis module is used to analyze the control process corresponding to the current thermal management condition based on the candidate calibration object, and to determine the problem nodes that affect the current thermal management condition. The scheme generation module is used to generate at least one candidate calibration scheme based on the problem node; The security verification module is used to perform security constraint verification on the candidate calibration scheme; The safety arbitration module is used to arbitrate the candidate calibration schemes that have passed the safety constraint verification based on multiple thermal management control objectives, so as to obtain the target calibration scheme; The verification and version management module is used to generate a verification scheme based on the target calibration scheme, and update the association between the engineering objects and the corresponding calibration parameter versions based on the verification results.

[0015] To address the aforementioned technical problems, this application also provides a calibration and optimization device for a thermal management system of a new energy vehicle, comprising: Memory, used to store computer programs; The processor is used to execute the computer program to implement the steps of the calibration and optimization method for the new energy vehicle thermal management system described above.

[0016] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the calibration and optimization method for the new energy vehicle thermal management system described above.

[0017] The calibration and optimization method for the new energy vehicle thermal management system provided in this application first acquires engineering data such as demand data, communication data, calibration parameter data, and vehicle operation logs generated during the operation of the new energy vehicle thermal management system. The engineering data from different sources are then standardized to form a unified engineering object, and the relationships between these engineering objects are established. This method links the previously scattered engineering data, enabling subsequent calibration analysis to be based on the correspondence between engineering objects, reducing reliance on manual searching and experience-based judgment during calibration, and improving the efficiency of engineering data utilization. Based on this, this application identifies the current thermal management operating condition according to the vehicle operation logs and determines candidate calibration objects related to the current operating condition by combining the relationships between engineering objects. Furthermore, by analyzing the control processes corresponding to the candidate calibration objects, problem nodes affecting the current thermal management operating condition are located. This allows for the determination of calibration optimization directions for specific control links, rather than simply adjusting parameters based on operating results, which helps improve the accuracy of problem location and the effectiveness of the calibration scheme. Furthermore, this application performs safety constraint verification on the generated candidate calibration schemes and conducts comprehensive arbitration on the candidate calibration schemes that meet the safety constraint conditions. This ensures that the final determined calibration scheme not only meets the system safety requirements but also takes into account different control needs, reducing the control risks caused by parameter adjustments under complex thermal management conditions. A corresponding verification scheme is generated based on the target calibration scheme, which can be used to update the relationships between engineering objects and the calibration parameter versions, forming a closed-loop feedback update. Through the above methods, the workload of manual calibration is reduced, and the calibration efficiency and reliability of the new energy vehicle thermal management system are improved.

[0018] In addition, this application also provides an apparatus and medium that correspond to the calibration and optimization method of the above-mentioned new energy vehicle thermal management system, with the same effect. Attached Figure Description

[0019] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments 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.

[0020] Figure 1 This application provides a flowchart of a calibration and optimization method for a thermal management system of a new energy vehicle. Figure 2 A structural diagram of a calibration and optimization device for a thermal management system of a new energy vehicle provided in this application embodiment; Figure 3 This is a structural diagram of another calibration and optimization device for a new energy vehicle thermal management system provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0022] The core of this application is to provide a calibration and optimization method, device, and medium for a thermal management system of a new energy vehicle.

[0023] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] This application provides a calibration and optimization method for a thermal management system of a new energy vehicle, such as... Figure 1 As shown, it includes: S1: Obtain engineering data from the thermal management system of new energy vehicles. The engineering data shall include at least demand data, communication data, calibration parameter data, and vehicle operation logs. S2: Standardize the engineering data, generate multiple engineering objects, and establish the relationships between the multiple engineering objects; S3: Identify the current thermal management condition based on the vehicle operation log, and determine the candidate calibration object corresponding to the current thermal management condition based on the correlation relationship; S4: Analyze the control process corresponding to the current thermal management condition based on the candidate calibration objects, and determine the problem nodes affecting the current thermal management condition; S5: Generate at least one candidate calibration scheme based on the problem node; S6: Perform security constraint verification on candidate calibration schemes; S7: Arbitrate the candidate calibration schemes that have passed the safety constraint verification based on multiple thermal management control objectives to obtain the target calibration scheme; S8: Generate a verification scheme based on the target calibration scheme, and update the relationships between engineering objects and the corresponding calibration parameter versions based on the verification results.

[0025] It should be noted that the thermal management system of this embodiment can be applied to new energy vehicles such as pure electric vehicles, plug-in hybrid electric vehicles, and range-extended electric vehicles, as well as other vehicles with similar thermal management control requirements. The thermal management system of this embodiment generally includes a thermal management controller, a vehicle controller, a power battery management system, an electric compressor, an electronic expansion valve, an electric fan, a water pump, a PTC heater, and components such as temperature sensors, pressure sensors, and flow sensors. The aforementioned controllers and actuators interact with each other through an in-vehicle communication network to complete the cooling, heating, and heat distribution control under different vehicle operating conditions. The method can be deployed on a calibration engineering workstation, a bench server, a hardware-in-the-loop (HIL) server, a cloud engineering platform, or a vehicle data analysis platform, and connected to CANoe (a software for in-vehicle network development, simulation, testing, and analysis), INCA (a software for electronic control unit measurement, calibration, and diagnostics), CANape (a software for electronic control unit measurement, calibration, and data analysis), the HIL platform, a software-in-the-loop (SIL) simulation platform, or a controller calibration tool.

[0026] Because the thermal management system for new energy vehicles involves a large amount of engineering data, including requirement documents, communication data, calibration parameters, operation logs, and test records, and these data come from different sources, have different formats, and are expressed in different ways, this embodiment first acquires the engineering data of the new energy vehicle thermal management system. The requirement data may include development requirements such as functional requirements, control requirements, safety requirements, and performance requirements; the communication data may include communication information such as communication matrices, communication protocols, signal definitions, and data dictionaries; the calibration parameter data may include compressor control parameters, electronic expansion valve control parameters, electronic fan control parameters, water pump control parameters, protection thresholds, and other thermal management control parameters; the vehicle operation log may include control requests, actuator status, feedback signals, fault information, and test records collected during vehicle operation. This embodiment does not limit the specific source of the engineering data; the aforementioned engineering data can come from the vehicle development platform, as well as from real vehicle testing, bench testing, or historical development data.

[0027] Furthermore, the acquired engineering data is standardized to generate multiple engineering objects, and relationships are established between these objects. It should be noted that in this embodiment, an engineering object refers to a data entity extracted from the engineering data and uniformly described, used to represent different types of data content involved in the development of the thermal management system. By converting data from different sources into a unified form of engineering objects, previously scattered data can be correlated, providing a unified data foundation for subsequent analysis.

[0028] The relationships between engineering objects describe the correspondences or constraints between different engineering objects. For example, a certain thermal management condition may correspond to multiple control signals, a control signal may correspond to one or more actuators, and an actuator may correspond to one or more calibration parameters. Furthermore, different calibration parameters may be subject to different protection conditions. Therefore, by establishing relationships between engineering objects, subsequent analysis processes can quickly determine the relevant control objects based on the current thermal management condition, without needing to analyze all engineering data item by item, thereby improving the efficiency of engineering data utilization.

[0029] Furthermore, the current thermal management condition is identified based on the vehicle operation log, and candidate calibration objects corresponding to the current thermal management condition are determined based on the correlation between engineering objects. It should be noted that the thermal management condition in this embodiment may include passenger compartment cooling condition, battery thermal management condition, dual-operation cooling condition, heating condition, defrosting condition, electronic expansion valve reset condition, and other thermal management control conditions; this embodiment does not limit these. Candidate calibration objects refer to one or more calibration objects that are related to the current thermal management condition and may affect the current control results. They do not represent the final calibration parameters that need to be adjusted, but rather serve as a candidate range for subsequent analysis.

[0030] After identifying candidate calibration targets, the control process corresponding to each candidate calibration target is analyzed to determine the problem nodes affecting the current thermal management operating conditions. It should be noted that in this embodiment, problem nodes are not fault points, but rather control nodes in the control process that affect the achievement of the current thermal management target. For example, problem nodes can appear in the control request generation stage, the control permission stage, the actuator execution stage, the status feedback stage, or the protection limitation stage; this embodiment does not limit this. By identifying problem nodes, the reasons why the current thermal management operating conditions have not reached the target state can be clarified, providing a basis for subsequent calibration optimization, rather than simply adjusting calibration parameters based on the final operating results.

[0031] Furthermore, at least one candidate calibration scheme is generated based on the problem node. In this embodiment, the candidate calibration scheme may include one or more calibration objects to be adjusted and their corresponding adjustment directions or adjustment amounts. Different candidate calibration schemes may correspond to different adjustment methods or different control objectives. This embodiment does not limit the number of candidate calibration schemes or their generation method.

[0032] To ensure that the generated calibration scheme meets vehicle operation safety requirements, this embodiment further verifies the candidate calibration schemes based on safety constraints and analyzes the candidate calibration schemes that meet the safety constraints according to multiple thermal management control objectives to determine the target calibration scheme. These thermal management control objectives may include passenger compartment comfort objectives, battery thermal management objectives, vehicle energy consumption objectives, and system safety objectives, etc. This embodiment does not limit the specific number or type of control objectives.

[0033] Finally, a verification plan is generated based on the target calibration plan, and the relationships between engineering objects and the corresponding calibration parameter versions are updated according to the verification results. The verification plan includes the test conditions to be performed, test steps, and judgment conditions. When the verification is successful, the target calibration plan is determined as the valid version and recorded in the calibration version library. At the same time, the verification results are associated with the corresponding test conditions and calibration parameters for retrieval and reuse in subsequent calibration optimization.

[0034] In summary, the calibration and optimization method for the new energy vehicle thermal management system provided in this application first acquires engineering data such as demand data, communication data, calibration parameter data, and vehicle operation logs generated during the operation of the new energy vehicle thermal management system. It then standardizes the engineering data from different sources to form a unified engineering object and establishes the relationships between these engineering objects. This method links the previously scattered engineering data, enabling subsequent calibration analysis to be based on the correspondence between engineering objects, reducing reliance on manual searching and experience-based judgment during calibration, and improving the efficiency of engineering data utilization. Based on this, this application identifies the current thermal management operating condition based on the vehicle operation logs and determines candidate calibration objects related to the current operating condition by combining the relationships between engineering objects. Furthermore, by analyzing the control processes corresponding to the candidate calibration objects, it locates the problem nodes affecting the current thermal management operating condition. This allows for the determination of calibration optimization directions for specific control links, rather than simply adjusting parameters based on operating results, which helps improve the accuracy of problem location and the effectiveness of the calibration scheme. Furthermore, this application performs safety constraint verification on the generated candidate calibration schemes and conducts comprehensive arbitration on the candidate calibration schemes that meet the safety constraint conditions. This ensures that the final determined calibration scheme not only meets the system safety requirements but also takes into account different control needs, reducing the control risks caused by parameter adjustments under complex thermal management conditions. A corresponding verification scheme is generated based on the target calibration scheme, which can be used to update the relationships between engineering objects and the calibration parameter versions, forming a closed-loop feedback update. Through the above methods, the workload of manual calibration is reduced, and the calibration efficiency and reliability of the new energy vehicle thermal management system are improved.

[0035] In order to establish a correspondence between engineering data from different sources and to provide a unified data foundation for subsequent thermal management condition identification, candidate calibration object determination and calibration scheme generation, this embodiment further explains the engineering objects and the relationships between them. Multiple engineering objects include demand objects, operating condition objects, signal objects, actuator objects, calibration objects, protection objects and test objects. Establish relationships between multiple project objects, including: Establish the requirement association between requirement objects and operating condition objects, the operating condition identification association between operating condition objects and signal objects, the control association between actuator objects and signal objects, the calibration association between actuator objects and calibration objects, the constraint association between protection objects and actuator objects and calibration objects, and the verification association between test objects and operating condition objects and calibration objects.

[0036] In this embodiment, the engineering objects include requirement objects, operating condition objects, signal objects, actuator objects, calibration objects, protection objects, and test objects. Of course, in other embodiments, other engineering objects such as fault objects, version objects, communication objects, or vehicle configuration objects can be added according to actual development needs; this embodiment does not limit this.

[0037] The data content of each type of object is shown in Table 1 below.

[0038] Table 1 Examples of Project Object Properties

[0039] After establishing the engineering objects, it is also necessary to establish the relationships between them. In this embodiment, a demand relationship is established between demand objects and operating condition objects to represent the thermal management operating conditions corresponding to different demands. For example, when a demand object represents the demand for rapid cooling of the power battery, it can be associated with the battery cooling operating condition; when a demand object represents the demand for rapid cooling of the passenger compartment, it can be associated with the passenger compartment cooling operating condition; when both demands exist simultaneously, it can be associated with the dual-cooling operating condition. Of course, in other embodiments, one demand object can also be associated with multiple operating condition objects, and one operating condition object can also correspond to multiple demand objects.

[0040] Establish a correlation between operating condition objects and signal objects to identify operating conditions. When the thermal management system is in different operating states, the corresponding control requests, temperature states, pressure states, and execution feedback usually differ. Therefore, the current thermal management operating condition can be identified based on multiple signal objects associated with the operating condition object. For example, based on signals such as passenger compartment temperature requests, battery temperature requests, compressor operating status, and electronic expansion valve status, it can be determined whether the corresponding thermal management operating condition has been entered.

[0041] A control association is established between actuator objects and signal objects. Since each actuator corresponds to a specific control signal and feedback signal, by establishing a control association, the corresponding actuator can be quickly identified based on the control signal, and the execution feedback can be used to determine whether the actuator has completed the corresponding control action. For example, the compressor target speed signal corresponds to the compressor actuator, the electronic expansion valve opening signal corresponds to the electronic expansion valve actuator, and the electronic fan duty cycle signal corresponds to the electronic fan actuator, etc.

[0042] A calibration association is established between the actuator object and the calibration object. Since the control effect of the actuator is usually determined by the corresponding calibration parameters, a correspondence can be established between the actuator and the calibration object. For example, a compressor actuator can be associated with multiple calibration objects such as the compressor target speed parameter and the compressor control curve parameter; an electronic expansion valve actuator can be associated with multiple calibration objects such as the target opening parameter and the opening correction parameter. This embodiment does not limit the number of calibration objects that a single actuator can correspond to.

[0043] A constraint relationship is established between the protected object, the actuator object, and the calibration object. When the adjustment of actuator control or calibration parameters may trigger safety limits, it can be determined whether the current adjustment meets the safety requirements based on the corresponding protected object. For example, when the compressor target speed exceeds the allowable range, the protected object can restrict the corresponding calibration object from continuing to adjust; when the system pressure exceeds the safety threshold, the protected object can also restrict the electronic expansion valve or electronic fan from continuing to perform the corresponding control.

[0044] A verification association is established between the test object, the operating condition object, and the calibration object. When the calibration object is adjusted, the applicable verification operating condition can be automatically determined according to the corresponding operating condition object, and the corresponding test content can be generated according to the test object, so that the verification process can cover the main operating states corresponding to the calibration object, thereby improving the pertinence of calibration verification.

[0045] In summary, this embodiment establishes a unified engineering object and further establishes the relationship between different engineering objects, enabling the scattered demand data, communication data, calibration data and test data in the development of the new energy vehicle thermal management system to establish a corresponding relationship, providing a unified data foundation for subsequent candidate calibration object determination, problem node analysis and calibration scheme generation.

[0046] After identifying the current thermal management condition, it is necessary to further determine the candidate calibration objects corresponding to the current thermal management condition in order to narrow down the scope of subsequent calibration analysis and improve the efficiency of calibration analysis. Specifically, the candidate calibration objects corresponding to the current thermal management condition are determined based on the correlation relationship, including: Match the corresponding operating condition object according to the current thermal management operating condition; The actuator objects involved in the current thermal management condition control are determined based on the signal objects associated with the operating condition object; Candidate calibration objects are determined based on the calibration object associated with the actuator object; The adjustment constraints corresponding to the candidate calibration object are determined based on the protection object associated with the candidate calibration object.

[0047] First, the corresponding operating condition object is matched based on the current thermal management condition. This step refers to identifying the current thermal management condition based on signals such as power mode, thermal management request, compressor status, electronic expansion valve status, PTC status, and damper status in the vehicle operation log. For example, when the power mode is ON (on) or START (start status value), and both the passenger compartment cooling request and the battery cooling request are valid, it is identified as a dual-cooling condition and matched to the corresponding operating condition object. The above identification process can be implemented using rule engines, state machines, decision trees, temporal pattern matching, or a combination of multiple methods, and the operating condition identification result can be accompanied by a confidence level. When the confidence level is lower than a threshold, the operator is prompted for confirmation.

[0048] Furthermore, operating condition identification can be based on the following exemplary rules: If the power mode is ON or START, the vehicle is not in Over-the-Air (OTA) mode, the passenger compartment cooling request is valid, and the VCU thermal management request is not Cool, then it is identified as single passenger compartment cooling; if the power mode is ON or START, the vehicle is not in OTA mode, the passenger compartment cooling request is invalid, and the VCU thermal management request is Cool (cooling status value), then it is identified as single battery cooling; if the power mode is ON or START, the vehicle is not in OTA mode, the passenger compartment cooling request is valid, and the VCU thermal management request is Cool... If the passenger compartment heating request is valid and the VCU thermal management request is not "Heat" (heating status value), it is identified as passenger compartment heating. If the passenger compartment heating request is invalid and the VCU thermal management request is "Heat", it is identified as battery heating or battery series heating. If there is a strong control command input, it is identified as strong control debugging and enters the strong control safety verification process. If there is an electronic expansion valve reset request, power-on initialization, unknown valve position, or valve position verification before cooling start, it is identified as electronic expansion valve reset condition. If high pressure, low pressure, superheat, evaporator temperature, blower feedback, or compressor fault conditions are triggered, it is identified as protection or fault degradation condition. The above rules can be implemented using rule engines, state machines, decision trees, temporal pattern matching, or a combination of multiple methods. In addition, the condition identification result can be accompanied by a confidence level. When the confidence level is lower than the threshold, the system prompts the operator for confirmation.

[0049] Secondly, the actuator objects participating in the current thermal management control are determined based on the signal objects associated with the operating condition object. Since an operating condition identification association has been established between the operating condition object and the signal object, the key input and output signals under this operating condition can be obtained. Then, based on the control association between the signal object and the actuator object, it can be determined which actuator objects participate in the current control. For example, in a dual-operation refrigeration condition, the actuator objects participating in the control include the compressor, electronic expansion valve, electric fan, shut-off valve, and water pump.

[0050] Then, candidate calibration objects are determined based on the calibration objects associated with the actuator objects. Since calibration associations have been established between the actuator objects and the calibration objects, all calibration objects related to the aforementioned actuator objects can be obtained. For example, calibration objects associated with the compressor actuator object include compressor proportional-integral parameters, feedforward MAP, target speed limit, and speed ramp rate; calibration objects associated with the electronic expansion valve actuator object include initial opening, target superheat, and adjustment steps.

[0051] Finally, the adjustment constraints corresponding to the candidate calibration objects are determined based on the protection objects associated with them. Since a constraint association has been established between the protection objects and the calibration objects, the protection conditions that each candidate calibration object needs to meet when modified can be obtained. For example, the calibration object related to compressor speed is constrained by the high-pressure protection object and the VCU power allowance protection object, while the calibration object related to the electronic expansion valve opening is constrained by the low-pressure protection object and the superheat protection object.

[0052] Furthermore, the process of determining the candidate calibration objects can be achieved by automatically searching the relationship network, or by the system automatically expanding all calibration objects associated with the executor object based on the relationship after the operator specifies a certain executor object. This embodiment does not impose strict limitations.

[0053] In summary, this embodiment first determines the current thermal management condition based on the vehicle operation log, then gradually determines the corresponding signal objects, actuator objects, and calibration objects based on the correlation between engineering objects, and further determines the adjustment constraints corresponding to the candidate calibration objects in conjunction with the protection objects, so that the candidate calibration objects can accurately correspond to the current thermal management condition, providing a reliable data foundation for subsequent control process analysis and calibration scheme generation, while reducing the participation of irrelevant calibration objects in the analysis and improving the efficiency of calibration analysis.

[0054] To further analyze the control process corresponding to the current thermal management condition and identify the problem nodes affecting it, the control process corresponding to the current thermal management condition is analyzed based on the candidate calibration objects to determine the problem nodes affecting it, including: Obtain control request signals from the vehicle operation log and determine whether a current thermal management requirement has been generated; Obtain the corresponding control permission signal and determine whether the control request meets the execution conditions; Acquire actuator control commands and actuator feedback status, and determine whether the actuator responds to the corresponding control commands; Acquire the temperature, pressure, or flow signals corresponding to the actuator, and determine whether the execution result meets the control requirements corresponding to the current thermal management condition; Obtain protection status information and determine whether the control process is limited by protection conditions; Based on the correlation between control requests, control permissions, actuator responses, execution feedback, and protection status, identify the problem nodes affecting the current thermal management conditions.

[0055] It should be noted that, based on the analysis of the candidate calibration objects, the control process corresponding to the current thermal management condition is analyzed to identify the problem nodes affecting the current thermal management condition, including a segment-by-segment verification along the transmission path of control request, control permission, actuator response, execution feedback and protection status.

[0056] First, obtain the control request signals from the vehicle operation log and determine if a current thermal management requirement has been generated. This step involves checking whether a cooling or heating request has been issued from the passenger compartment or the battery side. For example, check whether the passenger compartment air conditioning (AC) request signal or the VCU thermal management request signal is valid. If the control request signal is invalid, it indicates that there is no corresponding thermal management requirement at present, and the problem node is located at the beginning of the request chain.

[0057] Secondly, the corresponding control permission signal is obtained, and it is determined whether the control request meets the execution conditions. This step refers to checking whether the VCU allows the compressor or PTC to work, whether the power limit is greater than zero, and whether the vehicle is in non-OTA mode, etc. If the control request is valid but the control permission signal is invalid, the problem node is located in the permission chain link, for example, the VCU power limit prevents the actuator from starting.

[0058] Next, the actuator control commands and actuator feedback status are acquired, and it is determined whether the actuator responds to the corresponding control commands. This step refers to comparing the actuator control commands output by the thermal management controller with the actual actuator feedback status. For example, has the compressor target speed signal been issued, and has the actual compressor speed followed? Has the electronic expansion valve target opening been issued, and has the actual number of steps been reached? If the actuator control command has been output but the actuator feedback status has not changed, the problem lies in the actuator response stage, which may be due to actuator failure or communication anomaly.

[0059] Next, the temperature, pressure, or flow signals corresponding to the actuator are acquired, and it is determined whether the execution result meets the control requirements of the current thermal management condition. This step refers to checking whether the actual physical quantities have reached the target values. For example, the evaporator temperature signal is acquired and compared with the target evaporator temperature, the battery inlet water temperature signal is acquired and compared with the target water temperature, and the high-pressure refrigerant pressure signal is acquired and compared with the protection threshold. If the actuator has responded but the temperature, pressure, or flow rate has not met the control requirements, the problem node is located in the execution feedback loop, indicating that the current calibration parameters may be causing control deviation.

[0060] Next, the protection status information is obtained, and it is determined whether the control process is restricted by protection conditions. This step refers to checking whether high-pressure protection, low-pressure protection, evaporator freeze protection, overheat protection, or compressor fault protection have been triggered. If the protection status information shows that the protection has been triggered, the problem node is located in the protection chain, and the current control process is interrupted or restricted for safety reasons.

[0061] Finally, based on the correlation between control requests, control permissions, actuator responses, execution feedback, and protection status, the problem nodes affecting the current thermal management conditions are identified. In reality, a problem node can be a single-stage failure or a complex problem caused by the coupling of multiple stages. For example, the slow cooling of the passenger compartment might be due to VCU power limitations leading to a lower allowable compressor speed, while the electric fan duty cycle MAP is set too conservatively in the high-pressure range, resulting in insufficient fan intervention when the high pressure is high. This triggers high-pressure protection to limit compressor output, creating a coupling problem between the permission chain and the protection chain.

[0062] Furthermore, the determination of the problem node can be presented in the form of a causal chain. For example, starting from the occupant cabin cooling request, it can sequentially show whether the Air Conditioning Module (ACM) sends a request to the VCU, whether the VCU allows it, whether the power limit is met, whether the compressor communication is normal, whether the high and low pressures are within the normal range, whether the blower feedback is effective, and finally whether the compressor control command is output, thereby helping operators quickly understand the root cause of the problem.

[0063] It should be noted that the signal analysis process also calculates power-related indicators, comfort-related indicators, and noise, vibration, and harshness (NVH) indicators. Power-related indicators include AC requested power, AC actual power, PTC requested power, PTC actual power, and VCU power limit, used to determine whether energy consumption exceeds the allowable range. Comfort-related indicators include cooling time, time to reach the target outlet air temperature, temperature fluctuation, and defrosting / defogging response time, used to evaluate the thermal comfort performance of the passenger cabin. NVH indicators include the time the compressor is in the frequency avoidance zone, the time the fan is in the frequency avoidance zone, and the number and duration of electronic expansion valve resets, used to evaluate noise and vibration performance. These indicators, together with temperature-related indicators, pressure-related indicators, superheat indicators, actuator indicators, and protection indicators, constitute a complete signal analysis result, providing multi-dimensional data support for problem attribution.

[0064] Further, corresponding candidate calibration schemes are generated for subsequent safety constraint verification and target scheme determination. At least one candidate calibration scheme is generated based on the problem node, including: Identify candidate calibration objects associated with the problem node; The adjustment direction of the candidate calibration object is determined based on the difference between the running state corresponding to the problem node and the target state; Retrieve historical calibration results from the historical calibration records that correspond to the current thermal management conditions; Determine the candidate adjustment amount corresponding to each candidate calibration object based on historical calibration results; At least one candidate calibration scheme is generated based on multiple candidate calibration objects and their corresponding candidate adjustment values.

[0065] First, identify the candidate calibration objects associated with the problem node. Since the problem node has been located to a specific actuator or signal object through association, the calibration objects that need adjustment can be automatically extracted based on the calibration association between the actuator and the calibration objects. For example, if the problem node is located in a high-voltage protection circuit and is related to an electric fan actuator, the associated candidate calibration objects include the fan duty cycle MAP and the condenser protection threshold.

[0066] Secondly, the adjustment direction of the candidate calibration object is determined based on the difference between the operating status and the target status corresponding to the problem node. This step refers to determining whether the calibration object should be increased or decreased, or whether a specific range of the MAP table needs to be modified, based on the positive or negative deviation between the actual value and the target value. For example, when the high pressure is high under high temperature conditions and the fan duty cycle is at a low level for a long time, the operating status shows that the high pressure is higher than the target safety margin. Therefore, the adjustment direction is to increase the fan duty cycle in the high pressure range or advance the fan intervention threshold.

[0067] Then, retrieve the historical calibration results corresponding to the current thermal management condition from the historical calibration records. Historical calibration records are stored in the calibration version library, including calibration modification records and verification conclusions from previous vehicle platforms and similar operating conditions. During the search, similarity matching can be performed based on the operating condition object name, problem phenomenon keywords, or signal deviation patterns. For example, retrieve records of a previous vehicle model where the fan duty cycle MAP was modified due to high pressure under high-temperature dual-operation cooling conditions.

[0068] Next, candidate adjustment amounts are determined for each candidate calibration object based on historical calibration results. These candidate adjustment amounts can be fixed step sizes, such as adjustments based on preset percentages or absolute values; they can also be proportional step sizes, such as adjustments calculated proportionally based on the deviation between the operating state and the target state; or they can be calculated by referring to successful modification amplitudes in historical calibration results and combining them with the current vehicle model's adaptation coefficient. For example, if historical records show that the high-pressure range of the fan duty cycle MAP increased by 10% under similar operating conditions, the current candidate adjustment amount can be corrected based on the condenser specification differences of the current vehicle model.

[0069] For any candidate calibration object, the system calculates a recommended adjustment amount. This adjustment amount is jointly determined by the target deviation, deviation duration, actuator response hysteresis, safety margin, historical case similarity, and vehicle model adaptation coefficient. For example, the recommended adjustment amount is equal to a function of the target deviation, deviation duration, actuator response hysteresis, safety margin, historical case similarity, and vehicle model adaptation coefficient, where this function can be a lookup table function, a rule function, a linear weighted function, an optimization function, or a machine learning model.

[0070] Finally, at least one candidate calibration scheme is generated based on multiple candidate calibration objects and their corresponding candidate adjustment values. When multiple candidate calibration objects exist, the system can generate a single-parameter adjustment scheme or a multi-parameter combined adjustment scheme. For example, for the problem of high pressure in dual-operation cooling, a single-parameter scheme that only adjusts the fan duty cycle MAP can be generated, or a two-parameter scheme that simultaneously adjusts the fan duty cycle MAP and the compressor speed limit can be generated.

[0071] Furthermore, the output of candidate calibration schemes includes not only parameter values, but also the reasons for recommendation, influencing operating conditions, influencing signals, safety risks, testing suggestions, and whether operator confirmation is required. For example, when the high-pressure cooling at high temperature and low speed is too high and the fan duty cycle remains at a low level for an extended period, the system outputs a candidate calibration scheme to increase the fan duty cycle in the high-pressure range. The reasons for recommendation are: the high-pressure rise rate exceeds the threshold and the fan intervention is insufficient; the influencing operating condition is high-temperature and low-speed cooling; the influencing signals are high-pressure refrigerant pressure and fan duty cycle; the safety risk is that it needs to be confirmed that increasing the fan duty cycle will not cause fan overcurrent; and the testing suggestion is to perform high-temperature and low-speed and high-temperature and high-speed verification operating conditions.

[0072] Table 2 below shows the correspondence between some problem types and candidate calibration schemes: Table 2. Correspondence between Problem Types and Candidate Calibration Schemes

[0073] Furthermore, candidate calibration schemes can also be generated using lookup table rules, Bayesian optimization, genetic algorithms, or model prediction. This embodiment does not impose strict limitations.

[0074] To avoid safety risks in the thermal management system of new energy vehicles due to parameter adjustments, and to accommodate different thermal management needs, this embodiment further explains the safety constraint verification of candidate calibration schemes and the process of determining the target calibration scheme. The candidate calibration schemes undergo safety constraint verification, and arbitration is performed based on multiple thermal management control objectives, including: Obtain the corresponding security constraints based on the protected object associated with the candidate calibration scheme; Candidate calibration schemes are verified based on safety constraints, and candidate calibration schemes that do not meet the safety constraints are filtered out. The impact of the remaining candidate calibration schemes on passenger compartment thermal management requirements, battery thermal management requirements, vehicle energy consumption, and system safety was determined. When multiple thermal management requirements can be met simultaneously, the target calibration scheme is determined based on the degree of completion of the requirements corresponding to each candidate calibration scheme. When multiple thermal management requirements cannot be met simultaneously, the target calibration scheme is determined based on the priority of each thermal management requirement under the current operating conditions.

[0075] First, obtain the corresponding safety constraints based on the protected objects associated with the candidate calibration scheme. These safety constraints include at least high-pressure constraints, low-pressure constraints, superheat constraints, evaporator freezing constraints, blower feedback constraints, VCU allowance constraints, compressor failure constraints, PTC constraints, frequency avoidance constraints, and execution sequence constraints. For example, the high-pressure constraint stipulates that the compressor speed should not be increased when the high-pressure refrigerant pressure is close to the threshold; the low-pressure constraint stipulates that the electronic expansion valve opening should be limited when the low pressure is abnormally low or the superheat is below the threshold; and the evaporator freezing constraint stipulates that the compressor output should be limited when the evaporator temperature is below the risk threshold.

[0076] Secondly, candidate calibration schemes are verified based on safety constraints, and those that do not meet these constraints are filtered out. This step involves substituting the parameter values ​​from the candidate calibration schemes into the safety rules for judgment. For example, if a candidate calibration scheme suggests significantly increasing the compressor speed, but the current high pressure is already close to the protection threshold, the scheme is filtered out because it violates the high pressure constraint; similarly, if a scheme suggests reducing the opening of the electronic expansion valve, but the current superheat is already at the safety boundary, further reducing the opening may lead to insufficient superheat, and this scheme is also filtered out.

[0077] Next, the impact of the remaining candidate calibration schemes on passenger compartment thermal management requirements, battery thermal management requirements, vehicle energy consumption, and system safety is determined. This step involves evaluating the performance of each candidate calibration scheme across different target dimensions through simulation calculations or signal trend prediction. For example, one scheme might rapidly bring the passenger compartment evaporator temperature close to the target value, thus meeting the passenger compartment thermal management requirements, but it could lead to increased compressor power consumption, affecting overall vehicle energy consumption; another scheme might prioritize battery cooling requirements, but this would result in a slightly slower cooling rate for the passenger compartment.

[0078] When multiple thermal management requirements can be met simultaneously, the target calibration scheme is determined based on the degree of requirement fulfillment for each candidate calibration scheme. The degree of requirement fulfillment can be quantified by the deviation between the actual value and the target value, the time required to reach the target value, or the fluctuation range after stabilization. For example, under conditions of sufficient high-pressure margin and lenient power constraints, if scheme A reduces the temperature deviation of the crew cabin to within 0.5 degrees Celsius in five minutes, while scheme B requires ten minutes, then scheme A has a higher degree of requirement fulfillment and is selected as the target calibration scheme.

[0079] When multiple thermal management requirements cannot be met simultaneously, the target calibration scheme is determined based on the priority of each requirement under the current operating conditions. Priority can be dynamically determined based on factors such as the battery's maximum temperature, the magnitude of passenger compartment deviation, the urgency of defrosting requirements, or power limitation status. For example, under dual-cooling operation, if the battery's maximum temperature is approaching the thermal runaway risk threshold while the passenger compartment deviation is still within an acceptable range, the priority of battery thermal management requirements takes precedence over passenger compartment thermal management requirements. In this case, the candidate calibration scheme that satisfies battery cooling is selected as the target calibration scheme. Conversely, if the passenger compartment is in an urgent defrosting / defogging state, the priority of passenger compartment thermal management requirements is increased.

[0080] In addition, the arbitration process can also be implemented using a weighted operator method, that is, synthesizing the final target speed from the passenger cabin candidate speed, the battery-side candidate speed, and the safety-limited speed according to dynamic weights; or it can be implemented using a maximum value plus safety correction method, that is, taking the larger value among the candidate speeds from the demand side, but not exceeding the safety upper limit jointly determined by high voltage, low voltage, overheat, and power limits. This embodiment does not impose strict limitations.

[0081] It should be noted that the security constraints involved in security verification can be implemented through rule tables, state machines, or model prediction methods, specifically including the constraint descriptions shown in Table 3 below: Table 3 Safety Constraint Description

[0082] To further illustrate the application of this embodiment, the following description is based on a typical scenario in the thermal management system of new energy vehicles.

[0083] In one implementation, when the vehicle is in dual-cooling mode, i.e., when there is simultaneous cooling demand for the passenger compartment and cooling demand for the power battery, the passenger compartment temperature demand, battery temperature demand, compressor status, electronic expansion valve status, and system pressure status can be acquired separately. Based on this information, multiple candidate calibration schemes are generated, such as adjusting the compressor target speed, adjusting the electronic expansion valve opening, adjusting the electric fan operating parameters, or adjusting the corresponding parameter combinations of multiple actuators. Subsequently, each scheme is verified according to high-pressure limits, battery temperature limits, and vehicle energy consumption requirements, and the target calibration scheme is determined from the schemes that meet the safety conditions.

[0084] In another implementation, when forced control debugging is required, such as verifying the operation of a certain actuator in a specified state, the target actuator, target control parameters, and current vehicle state can be obtained first, and it can be determined whether the current state meets the execution conditions. When the conditions are met, the test can be executed according to the preset control parameters; when a state that may affect vehicle safety is detected, the corresponding control operation is restricted or the execution method is adjusted. In this way, the verification of a specific control state can be completed while ensuring vehicle safety.

[0085] In another implementation, when electronic expansion valve reset optimization is required, candidate adjustment schemes can be determined based on the electronic expansion valve's historical operating position, current thermal management conditions, and system operating status. For example, after vehicle startup, the initial position of the electronic expansion valve can be determined based on the current ambient temperature, pressure status, and historical operating position; during vehicle operation, the control parameters of the electronic expansion valve can also be adjusted based on pressure changes and heat exchange status, thereby reducing control deviations caused by an unreasonable initial position.

[0086] It should be noted that the above-described dual-cooling, forced control debugging, and electronic expansion valve reset are only specific application methods of this embodiment and do not mean that this embodiment is only applicable to the above scenario. For other thermal management conditions with multiple control requirements, multiple actuators involved in control, or parameter optimization required, the above-described safety verification and scheme selection methods can also be adopted.

[0087] To verify whether the target calibration scheme meets the control requirements of the current thermal management conditions, and to apply the verification results to subsequent calibration optimization, a verification scheme is generated based on the target calibration scheme. The relationships between engineering objects and the corresponding calibration parameter versions are updated based on the verification results, including: The associated actuator objects, operating condition objects, and test objects are determined based on the target calibration scheme; Generate corresponding verification conditions, test steps, and verification criteria based on the working conditions and test objects; The verification plan was executed and the verification results were obtained. When the verification results meet the verification conditions, the calibration parameters corresponding to the target calibration scheme are determined as the valid version, and the target calibration scheme, applicable working conditions and verification results are associated. When the verification result does not meet the verification conditions, the verification result is associated with the corresponding calibration parameter version for subsequent calibration optimization.

[0088] First, determine the associated actuator objects, operating condition objects, and test objects based on the target calibration scheme. Since different target calibration schemes correspond to different adjustments, their scope of influence also varies.

[0089] Furthermore, corresponding verification conditions, test steps, and verification conditions are generated based on the operating conditions and test objects. Verification conditions describe the vehicle operating states that the target calibration scheme needs to verify, such as high-temperature cooling conditions, low-temperature heating conditions, dual-cooling conditions, and battery cooling conditions. Test steps describe the control operations, data acquisition processes, and execution flows during the test. Verification conditions are used to determine whether the target calibration scheme meets the expected requirements.

[0090] For example, when modifying the initial opening of the electronic expansion valve and the compressor arbitration weight in dual-mode refrigeration, the system identifies the affected objects including dual-mode refrigeration entry conditions, compressor speed, electronic expansion valve opening, high pressure, low pressure, superheat, fan duty cycle, shut-off valve status, VCU power limit, and protection recovery logic, and generates the regression test items shown in Table 4 below: Table 4 Examples of Regression Test Items

[0091] Each regression test item includes preconditions, input signal sequence, forced control or simulated action, expected output signal, decision threshold, sampling time, pass or fail rules, and associated parameters.

[0092] Furthermore, the generated verification scheme is executed, and the corresponding verification results are obtained. Verification results may include temperature change results, pressure change results, actuator response results, energy consumption change results, and protection status change results. For example, after adjusting the compressor control parameters, the adjustment effect can be judged by comparing the cooling rate before and after the adjustment, the time to reach the target temperature, and the system pressure change; after adjusting the electronic expansion valve control parameters, the rationality of the adjustment can be judged by comparing the heat exchange effect, pressure stability, and evaporator temperature status.

[0093] When the verification results meet the verification conditions, the calibration parameters corresponding to the target calibration scheme are determined as the valid version, and a correlation is established between the target calibration scheme, applicable operating conditions, and verification results. The valid version here refers to the calibration parameter status that has been verified to meet the current development requirements. This can include information such as parameter modifications, applicable vehicle configurations, applicable operating conditions, verification time, and verification results. By establishing the above correlation, a reference can be provided for generating candidate calibration schemes when similar or identical thermal management operating conditions occur subsequently.

[0094] When the verification result does not meet the verification conditions, the verification result is associated with the corresponding calibration parameter version for subsequent calibration optimization.

[0095] In summary, this embodiment automatically generates a verification scheme based on the target calibration scheme and updates the associated information of the engineering object and the version of the calibration parameters according to the verification results. This enables the calibration process to form a continuous optimization process from problem analysis, scheme determination, testing and verification to result saving. Through this method, repetitive testing and manual processing can be reduced, the efficiency of calibration data utilization in the new energy vehicle thermal management system can be improved, and the reliability of calibration work under different vehicle models and operating conditions can be enhanced.

[0096] This embodiment uses a dual-cooling operation as an example to further illustrate the specific implementation of multi-objective arbitration. A dual-cooling operation refers to a scenario where occupant cabin cooling and battery cooling requests coexist, requiring the compressor, electronic expansion valve, electric fan, and shut-off valve to work in coordination.

[0097] In this scenario, the system calculates the cooling requirements of the passenger compartment, the cooling requirements of the battery, the available power margin, and the safety margin. The cooling requirements of the passenger compartment are determined based on the deviation between the actual evaporator temperature and the target evaporator temperature; the cooling requirements of the battery are determined based on the deviation between the battery inlet water temperature and the target water temperature, or the deviation between the battery-side low pressure and the target low pressure; the available power margin is determined based on the difference between the VCU power limit and the actual power consumed; and the safety margin is determined based on the high pressure, low pressure, superheat, evaporator temperature, and compressor fault status.

[0098] The multi-objective arbitration process for dual-operation cooling is as follows: First, confirm that both the passenger compartment cooling request and the battery cooling request are valid simultaneously. Second, calculate the candidate speeds for the passenger compartment and the battery side, respectively. Third, calculate battery priority, passenger compartment priority, safety risk, and power margin. Fourth, determine the target compressor speed based on priority and safety risk. Fifth, determine the initial opening and adjustment steps of the electronic expansion valve based on the target superheat, actual superheat, and battery temperature. Sixth, determine the electronic fan duty cycle based on high pressure, vehicle speed, ambient temperature, and compressor load. Seventh, determine the shut-off valve status based on evaporator protection, blower feedback, and passenger compartment demand. Eighth, output the strategy after passing the safety verification; if it fails, execute a degradation scheme, such as limiting the speed, shutting down the compressor, shutting down the electronic expansion valve, or delaying the fan.

[0099] Based on the above requirements, the system generates candidate compressor speeds for the passenger compartment side, candidate compressor speeds for the battery side, and a safety-limited speed. During arbitration, if the battery temperature is close to the safety risk threshold while the passenger compartment deviation is still within an acceptable range, the candidate compressor speed for the battery side is prioritized; if the passenger compartment is in a defrosting emergency, the weight of the passenger compartment side is increased. The arbitration result can be a speed selected based on a priority operator, a weighted sum of the speeds on both sides based on dynamic weights, or the larger value of the two speeds that does not exceed the safety-limited speed.

[0100] The opening degree of the electronic expansion valve is calculated based on the target superheat and the actual superheat according to the battery thermal management requirements, including the initial opening degree and the number of adjustment steps. When the evaporator on the passenger compartment side approaches the freeze protection or the blower feedback is abnormal, the safety constraint requires the closure or restriction of the shut-off valve. The duty cycle of the electronic fan is determined by the high pressure, ambient temperature, vehicle speed and compressor load. When the high pressure rise rate exceeds the threshold, the fan duty cycle is increased in advance.

[0101] Once the arbitration result passes the security check, the final control strategy is output; if it fails, a degradation scheme is executed, such as limiting the speed, shutting down the compressor, shutting down the electronic expansion valve, or delaying the fan operation.

[0102] Resetting an electronic expansion valve is a special operating condition. Traditionally, the valve is directly driven back to the fully closed position after the controller is powered on to determine the valve position, but this can generate significant noise. Delaying the reset may lead to refrigerant migration, low-pressure anomalies, or even liquid slugging under low-temperature or unbalanced pressure conditions. To address these issues, this embodiment provides an adaptive reset strategy for the electronic expansion valve.

[0103] The system acquires the electronic expansion valve's last target opening degree, last actual number of steps or estimated position, compressor status at the last shutdown, last reset time, current ambient temperature, current high pressure, current low pressure, pressure difference, current low pressure temperature, actual superheat, whether there is a cooling request, whether there is a battery cooling request, whether it is in a low temperature environment, whether there is a noise-sensitive state and historical reset noise records, and calculates the position reliability, liquid slugging risk, pressure balance degree and noise risk.

[0104] If the location reliability is low, there is a cooling start request, and the risk of liquid slugging is high, then choose to reset immediately or prohibit the compressor from starting. Start-up is allowed only after the reset is completed and the superheat is safe. If the location reliability is high, the pressure has been balanced, there is no cooling request, and the noise risk is high, then skip this reset or delay the reset. If the location reliability is moderate, the pressure difference is small, but a cooling request is expected in the short term, then use low-speed segmented reset or small-step calibration. If refrigerant migration may occur in low-temperature environments, and the low-pressure temperature is close to or below the saturation temperature, then first prohibit the compressor from starting, execute the electronic expansion valve reset or keep the electronic expansion valve closed, and wait for the superheat to recover. If abnormal current, step loss, abnormal pressure, or excessive noise is detected during the reset process, then the reset is stopped and a fault prompt is entered.

[0105] The electronic expansion valve reset decision output includes immediate reset, delayed reset, segmented reset, low-speed reset, skip reset, disable compressor start, and allow start after reset. The system records the decision basis, execution time, execution result, pressure change, and noise feedback for each reset to the historical calibration record for reuse in similar scenarios.

[0106] In actual calibration and commissioning processes, operators sometimes need to directly control actuators such as compressors, electronic expansion valves, shut-off valves, electronic fans, water pumps, or PTCs, i.e., issue force control commands. To reduce safety risks during force control commissioning, this embodiment provides a method for secure interception and step-by-step execution of force control commands.

[0107] A forced control command includes the actuator object identifier, target value, duration, target operating condition, operator information, and timestamp. Furthermore, the forced control command also includes a forced control reason field, used to record the purpose of performing the forced control operation. A complete forced control command can be represented as structured data containing the actuator object, target value, duration, target operating condition, operator, timestamp, and forced control reason. For example, when an operator inputs a forced control command for the compressor to 3000 rpm, the forced control reason is recorded as "high temperature dual-operation refrigeration debugging," the operator is recorded as the current operator's account, the timestamp is recorded as the time the command was issued, and the target operating condition is recorded as "dual-operation refrigeration."

[0108] Before the control command is actually sent to the controller, the system first performs a precondition check, including determining whether the power mode is allowed, whether the vehicle is in OTA mode, whether the VCU allows the corresponding actuator to work, whether the power limit is greater than zero, whether the high pressure is within the allowable range, whether the low pressure is within the allowable range, whether the overheat is greater than the minimum safety threshold, whether the evaporator temperature is higher than the freeze protection threshold, whether the blower feedback is effective, whether the compressor communication is normal, whether there is a fault in the compressor or PTC, and whether the electronic expansion valve position is reliable.

[0109] Based on the inspection results, the system categorizes mandatory control commands into four types: executeable, conditionally permissible, degraded execution, and prohibited execution. Execution allowed indicates that all safety conditions are met, and the command can be executed directly and enter real-time monitoring. Conditionally permissible indicates a minor risk, but this risk can be mitigated through step-by-step actions, such as first turning on the electric fan, then opening the electronic expansion valve, and waiting for the pressure to stabilize before starting the compressor. Degraded execution indicates that the original command's target value is too high or the action is too rapid; the system automatically downgrades the target value, such as reducing the compressor speed target, decreasing the electronic expansion valve opening, or shortening the duration. Prohibited execution indicates a significant risk such as liquid slugging, high pressure, low pressure, freezing, malfunction, or VCU disallowance; the system blocks the command and provides a reason prompt.

[0110] During the execution of the strict control, the system continuously monitors key feedback signals. If situations such as high pressure exceeding the threshold, low pressure abnormality, low superheat, evaporator nearing freezing, compressor failure, or VCU permission signal revocation occur, the system immediately executes a rollback action, such as reducing the speed, shutting down the compressor, closing the electronic expansion valve, maintaining fan operation with a delay, or shutting down the PTC.

[0111] Furthermore, the aforementioned security interception process can be implemented using a personal computer (PC) calibration tool, HIL platform, bench controller, or cloud platform; the issuance of strong control commands should be confirmed by the operator and should not directly bypass existing vehicle safety rules. This embodiment does not impose strict limitations.

[0112] This embodiment further illustrates the specific implementation method of version tracing and knowledge reinjection.

[0113] The system generates a unique record for each calibration and optimization process. The record includes the problem number, vehicle platform, software version, calibration version, vehicle operation log file, operating condition identification result, problem attribution chain, candidate calibration scheme, parameter values ​​before and after modification, safety verification result, operator confirmation information, test object, verification conclusion, whether it is adopted for mass production, whether it is rolled back, and subsequent issues.

[0114] When the verification results meet the verification conditions, the system records this calibration and optimization process as a successful case in the historical calibration record, including the target calibration scheme, applicable working conditions, and the correlation between the verification results, forming a reusable positive sample. When the verification results do not meet the verification conditions or are rejected by the operator, the system records the reason for the failure and the specific conditions that were not met, serving as a negative example constraint for subsequent calibration and optimization.

[0115] Through version tracing and knowledge reinjection, calibration experience across different vehicle platforms, thermal management topologies, and software versions can be accumulated. When encountering similar operating conditions or problems in the future, the system can retrieve historical calibration records, refer to past successful cases, or avoid past failures, thereby improving cross-vehicle reuse efficiency.

[0116] Furthermore, version records can be stored in a local database, server storage, cloud storage, or other non-transitory computer-readable media. This embodiment does not impose strict limitations.

[0117] To provide a more complete explanation of the data structure for version management, this embodiment supplements the definitions of the Issue object and the Version object.

[0118] Issue objects are used to record problems found during calibration, including problem symptoms, operating conditions, time range, associated signal segments, suspected causes, recommended modifications, verification results, and shutdown status. For example, if the system analyzes the vehicle operation log and finds that the high pressure is too high when the dual-cooling system is operating at high temperatures, an Issue object is generated. The problem symptom is recorded as "high pressure," the operating condition is recorded as "dual-cooling system," the time range is recorded as the start and end times of the high pressure exceeding the threshold in the log, the associated signal segments are recorded as the time-series data of high-pressure refrigerant pressure, compressor speed, and fan duty cycle, the suspected cause is recorded as "fan duty cycle MAP high-pressure range setting is too low," the recommended modification is recorded as "increase fan duty cycle MAP," the verification result is recorded as "to be tested," and the shutdown status is recorded as "not shut down."

[0119] The Version object is used to record calibration parameter version information, including calibration version number, vehicle platform, software version, modification time, modifier, associated Issue object number, parameter values ​​before and after modification, safety verification results, operator confirmation information, test object, and verification conclusion. Through the association between the Issue object and the Version object, each calibration modification can be traced back to the specific source of the problem, the basis for the modification, and the verification conclusion, forming a complete engineering record.

[0120] This embodiment uses the example of slow cooling of the passenger compartment and high pressure in a high-temperature environment when the dual-cooling system is on to illustrate the complete calibration and optimization process.

[0121] Step 1: The data access module imports the corresponding test logs, current calibration version, communication data, and required data.

[0122] Step 2: The operating condition identification unit identifies that the log interval is in a dual-cooling condition, that is, the crew cabin cooling request is valid and the battery cooling request is valid.

[0123] Step 3: The signal analysis unit calculates the deviation between the actual evaporator temperature and the target evaporator temperature, the deviation between the battery inlet water temperature and the target water temperature, the high-pressure refrigerant pressure change rate, the low-pressure refrigerant pressure, the actual superheat, the target superheat, the compressor target speed, the actual speed, the electronic expansion valve opening, and the fan duty cycle.

[0124] Step 4: The passenger compartment unit determines that the passenger compartment cooling capacity is insufficient and suggests increasing the contribution of the passenger compartment side compressor speed or reducing the target evaporator temperature; the battery thermal management unit determines that the battery side still needs cooling and suggests maintaining or slightly increasing the opening of the electronic expansion valve; the energy consumption unit determines that the VCU power limit is close to the upper limit and suggests avoiding simply increasing the compressor speed; the safety verification unit determines that the high pressure is too high and suggests prioritizing increasing the fan duty cycle or adjusting the electronic expansion valve strategy.

[0125] Step 5: After considering the above opinions, the arbitration unit outputs the following candidate solutions: moderately increase the duty cycle of the electric fan in the high-voltage range, slightly adjust the priority weight of dual-cooling, increase the target superheat in the high battery temperature range, limit the slope of the compressor speed increase, and prevent the high voltage from continuing to rise.

[0126] Step six: The safety verification unit checks that the candidate scheme will not cause the overheating to fall below the safety threshold, will not exceed the compressor's maximum speed, will not violate the VCU power limit, and will not trigger the evaporator freeze protection.

[0127] Step 7: The test generation unit generates regression operating conditions such as high temperature and low speed, high temperature and medium speed, high temperature and idle speed, VCU power limit, passenger compartment priority, battery priority, and protection recovery.

[0128] Step 8: After the operator confirms, the system generates a calibration change report and records it in the version traceability module.

[0129] The above embodiments have described in detail the calibration and optimization method for the thermal management system of new energy vehicles. This application also provides embodiments corresponding to the calibration and optimization device for the thermal management system of new energy vehicles. It should be noted that this application describes the embodiments of the device from two perspectives: one is based on functional modules, and the other is based on hardware.

[0130] From the perspective of functional modules Figure 2 A structural diagram of a calibration and optimization device for a new energy vehicle thermal management system provided in this application embodiment is shown below. Figure 2 As shown, a calibration and optimization device for a thermal management system of a new energy vehicle includes: Data processing module 21 is used to acquire engineering data of the thermal management system of new energy vehicles. The engineering data includes at least demand data, communication data, calibration parameter data and vehicle operation logs. The working condition analysis module 22 is used to standardize the engineering data, generate multiple engineering objects, and establish the relationship between the multiple engineering objects. The operating condition identification module 23 is used to identify the current thermal management operating condition based on the vehicle operation log, and determine the candidate calibration object corresponding to the current thermal management operating condition based on the correlation relationship; Control process analysis module 24 is used to analyze the control process corresponding to the current thermal management condition based on the candidate calibration object, and to determine the problem nodes affecting the current thermal management condition. Solution generation module 25 is used to generate at least one candidate calibration solution based on the problem node; The security verification module 26 is used to perform security constraint verification on the candidate calibration scheme; Safety arbitration module 27 is used to arbitrate candidate calibration schemes that have passed safety constraint verification based on multiple thermal management control objectives, and obtain the target calibration scheme; The verification and version management module 28 is used to generate a verification scheme based on the target calibration scheme, and update the relationship between engineering objects and the corresponding calibration parameter version based on the verification results.

[0131] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0132] Figure 3 A structural diagram of another calibration and optimization device for a new energy vehicle thermal management system provided in this application embodiment is shown below. Figure 3 As shown, the calibration and optimization device for the thermal management system of new energy vehicles includes: a memory 30 for storing computer programs; The processor 31 is used to execute a computer program to implement the steps of the method for obtaining user operation habit information as described in the above embodiment (calibration and optimization method for thermal management system of new energy vehicle).

[0133] The calibration and optimization device for the new energy vehicle thermal management system provided in this embodiment may include, but is not limited to, mobile terminals, personal computers, workstations, etc.

[0134] The processor 31 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 31 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 31 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0135] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 30 is used to store at least the following computer program 301, which, after being loaded and executed by the processor 31, can implement the relevant steps of the calibration and optimization method for the new energy vehicle thermal management system disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, and the storage method may be temporary or permanent storage. The operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include, but is not limited to, the data involved in implementing the calibration and optimization method for the new energy vehicle thermal management system.

[0136] In some embodiments, the calibration and optimization device for the thermal management system of a new energy vehicle may further include a display screen 32, an input / output interface 33, a communication interface 34, a power supply 35, and a communication bus 36.

[0137] Those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the calibration and optimization device for the thermal management system of new energy vehicles, and may include more or fewer components than shown.

[0138] The calibration and optimization device for a new energy vehicle thermal management system provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: calibration and optimization method for a new energy vehicle thermal management system.

[0139] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above embodiment of the calibration and optimization method for the new energy vehicle thermal management system.

[0140] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] The computer-readable storage medium provided in this embodiment stores a computer program. When the processor executes the program, it can implement the following method: a calibration and optimization method for a thermal management system of a new energy vehicle.

[0142] The calibration and optimization method, apparatus, and medium for the new energy vehicle thermal management system provided in this application have been described in detail above. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

[0143] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

Claims

1. A calibration and optimization method for a thermal management system of a new energy vehicle, characterized in that, include: Acquire engineering data for the thermal management system of new energy vehicles, wherein the engineering data includes at least demand data, communication data, calibration parameter data, and vehicle operation logs; The engineering data is standardized to generate multiple engineering objects, and the relationships between the multiple engineering objects are established. The current thermal management condition is identified based on the vehicle operation log, and candidate calibration objects corresponding to the current thermal management condition are determined based on the correlation. Based on the analysis of the candidate calibration objects, the control process corresponding to the current thermal management condition is determined, and the problem nodes affecting the current thermal management condition are identified. At least one candidate calibration scheme is generated based on the problem node; The candidate calibration schemes are subjected to security constraint verification. Arbitrate the candidate calibration schemes that have passed the safety constraint verification based on multiple thermal management control objectives to obtain the target calibration scheme; A verification scheme is generated based on the target calibration scheme, and the relationships between the engineering objects and the corresponding calibration parameter versions are updated based on the verification results.

2. The calibration and optimization method for the thermal management system of a new energy vehicle according to claim 1, characterized in that, The multiple engineering objects include requirement objects, operating condition objects, signal objects, actuator objects, calibration objects, protection objects, and test objects; Establishing relationships between multiple project objects includes: Establish the requirement association between requirement objects and operating condition objects, the operating condition identification association between operating condition objects and signal objects, the control association between actuator objects and signal objects, the calibration association between actuator objects and calibration objects, the constraint association between protection objects and actuator objects and calibration objects, and the verification association between test objects and operating condition objects and calibration objects.

3. The calibration and optimization method for the thermal management system of a new energy vehicle according to claim 1, characterized in that, The step of determining the candidate calibration object corresponding to the current thermal management condition based on the association relationship includes: Match the corresponding operating condition object according to the current thermal management operating condition; The actuator object participating in the current thermal management condition control is determined based on the signal object associated with the operating condition object; Candidate calibration objects are determined based on the calibration object associated with the actuator object; The adjustment constraints corresponding to the candidate calibration object are determined based on the protection object associated with the candidate calibration object.

4. The calibration and optimization method for the thermal management system of a new energy vehicle according to claim 1, characterized in that, The step of analyzing the control process corresponding to the current thermal management condition based on the candidate calibration object, and determining the problem nodes affecting the current thermal management condition, includes: Obtain control request signals from the vehicle operation log and determine whether a current thermal management requirement has been generated; Obtain the corresponding control permission signal and determine whether the control request meets the execution conditions; Acquire actuator control commands and actuator feedback status, and determine whether the actuator responds to the corresponding control commands; Acquire the temperature, pressure, or flow signals corresponding to the actuator, and determine whether the execution result meets the control requirements corresponding to the current thermal management condition; Obtain protection status information and determine whether the control process is limited by protection conditions; Based on the correlation between control requests, control permissions, actuator responses, execution feedback, and protection status, identify the problem nodes affecting the current thermal management conditions.

5. The calibration and optimization method for the thermal management system of a new energy vehicle according to claim 1, characterized in that, The step of generating at least one candidate calibration scheme based on the problem node includes: Identify candidate calibration objects associated with the problem node; The adjustment direction of the candidate calibration object is determined based on the difference between the running state and the target state corresponding to the problem node; Retrieve historical calibration results from the historical calibration records that correspond to the current thermal management conditions; Based on the historical calibration results, determine the candidate adjustment amount corresponding to each candidate calibration object; At least one candidate calibration scheme is generated based on multiple candidate calibration objects and their corresponding candidate adjustment values.

6. The calibration and optimization method for a new energy vehicle thermal management system according to claim 1, characterized in that, The step of performing safety constraint verification on the candidate calibration scheme and arbitrating based on multiple thermal management control objectives includes: Obtain the corresponding security constraints based on the protected object associated with the candidate calibration scheme; The candidate calibration schemes are verified according to the aforementioned security constraints, and candidate calibration schemes that do not meet the security constraints are filtered out. The impact of the remaining candidate calibration schemes on passenger compartment thermal management requirements, battery thermal management requirements, vehicle energy consumption, and system safety was determined. When multiple thermal management requirements can be met simultaneously, the target calibration scheme is determined based on the degree of completion of the requirements corresponding to each candidate calibration scheme. When multiple thermal management requirements cannot be met simultaneously, the target calibration scheme is determined based on the priority of each thermal management requirement under the current operating conditions.

7. The calibration and optimization method for a new energy vehicle thermal management system according to claim 1, characterized in that, The step of generating a verification scheme based on the target calibration scheme, and updating the association between the engineering objects and the corresponding calibration parameter versions based on the verification results, includes: The associated actuator objects, operating condition objects, and test objects are determined based on the target calibration scheme; Generate corresponding verification conditions, test steps, and verification criteria based on the described working conditions and test objects; The verification results were obtained by executing the verification scheme. When the verification results meet the verification conditions, the calibration parameters corresponding to the target calibration scheme are determined as the valid version, and the target calibration scheme, applicable working conditions and verification results are associated. When the verification result does not meet the verification conditions, the verification result is associated with the corresponding calibration parameter version for subsequent calibration optimization.

8. A calibration and optimization device for a thermal management system of a new energy vehicle, characterized in that, include: The data processing module is used to acquire engineering data of the thermal management system of new energy vehicles. The engineering data includes at least demand data, communication data, calibration parameter data, and vehicle operation logs. The working condition analysis module is used to standardize the engineering data, generate multiple engineering objects, and establish the relationship between the multiple engineering objects. The operating condition identification module is used to identify the current thermal management operating condition based on the vehicle operation log, and to determine the candidate calibration object corresponding to the current thermal management operating condition based on the association relationship; The control process analysis module is used to analyze the control process corresponding to the current thermal management condition based on the candidate calibration object, and to determine the problem nodes that affect the current thermal management condition. The scheme generation module is used to generate at least one candidate calibration scheme based on the problem node; The security verification module is used to perform security constraint verification on the candidate calibration scheme; The safety arbitration module is used to arbitrate the candidate calibration schemes that have passed the safety constraint verification based on multiple thermal management control objectives, so as to obtain the target calibration scheme; The verification and version management module is used to generate a verification scheme based on the target calibration scheme, and update the association between the engineering objects and the corresponding calibration parameter versions based on the verification results.

9. A calibration and optimization device for a thermal management system of a new energy vehicle, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the calibration and optimization method of the new energy vehicle thermal management system as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the calibration and optimization method for the thermal management system of a new energy vehicle as described in any one of claims 1 to 7.

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