Vehicle system simulation parameter adjustment method and device based on Modelica

Abstract

The present application discloses a vehicle system simulation parameter adjustment method and device based on Modelica, which relates to the field of simulation parameter adjustment. The method includes obtaining various causal simulation equations and vehicle simulation system output variables in the simulation solution process of a vehicle simulation system built based on Modelica; determining a target output variable based on the output variable of the vehicle simulation system; determining constants and state variables that affect the target output variable in the causal simulation equation based on the target output variable and each causal simulation equation, and obtaining parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable; adjusting the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable until the target output variable reaches the expected output value. The present application searches for various parameters that affect the target output variable through the causal simulation equations in the simulation solution process, and can accurately and quickly find various parameters that affect the target output variable from a large number of parameters, thereby improving the efficiency of simulation debugging.

Description

Technical Field

[0001] The present application relates to the field of simulation parameter adjustment, and in particular to a vehicle system simulation parameter adjustment method and device based on Modelica. Background Art

[0002] To ensure the performance of various vehicle systems, the Modelica modeling language is typically used to build a vehicle simulation model for the simulation process of vehicle performance control. Modeling with the Modelica modeling language is non-causal, and the equation variable system spans multiple models, collectively constructing the differential equations for the entire vehicle simulation system. When using non-causal simulation equations to build system models, users can focus on describing the model's physical equation processes rather than the specific differential equation system solution, reducing the modeling difficulty. The corresponding differential equation system solution is handled by the software. However, because the actual solution process of the non-causal equation system is sequential, the actual simulation calculation process and the computational relationships between variables are handled by the simulation software and are currently invisible to the user.

[0003] When users discover during the simulation process that some of the model system's key output variables have undesirable numerical oscillations or calculate undesirable values, since the non-causal simulation equations used to build the model cannot clearly indicate the order in which variables are calculated, and since the entire equation system spans multiple model components, it is difficult to determine which constants, state variables, and differentials of state variables cause the calculation of the key output variables to differ significantly from the ideal state. This also makes it difficult to determine the parameter adjustment direction when debugging the model system's key variables, resulting in a lot of time spent on adjusting various parameters in the model output results and parameter debugging, and low parameter debugging efficiency during the simulation process. Summary of the Invention

[0004] The purpose of this application is to provide a vehicle system simulation parameter adjustment method and device based on Modelica, which can accurately and quickly find the parameters that affect the target output variable from a large number of parameters, thereby improving the debugging efficiency of the simulation process.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a vehicle system simulation parameter adjustment method based on Modelica, comprising:

[0007] Obtain the causal simulation equations used in the simulation and solution process of a vehicle simulation system built based on Modelica; the causal simulation equations are used to describe the physical processes of various vehicle systems; the causal simulation equations include constants, state variables of the vehicle simulation system, simulation intermediate variables, and output variables of the vehicle simulation system;

[0008] Determine target output variables according to output variables of the vehicle simulation system;

[0009] Determine the constants and state variables that affect the target output variables in the causal simulation equations according to the target output variables and each causal simulation equation, and obtain the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variables;

[0010] Adjust the parameter constants and parameter state variables corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value.

[0011] In a second aspect, the present application provides a vehicle system simulation and parameter adjustment device based on Modelica, comprising:

[0012] The data acquisition module is used to obtain the causal simulation equations used in the simulation and solution process of the vehicle simulation system built based on Modelica. The causal simulation equations are used to describe the physical processes of various vehicle systems. The causal simulation equations include constants, state variables of the vehicle simulation system, simulation intermediate variables, and output variables of the vehicle simulation system.

[0013] A target variable determination module, used for determining a target output variable according to an output variable of a vehicle simulation system;

[0014] An influencing variable determination module is used to determine the constants and state variables that affect the target output variables in the causal simulation equations based on the target output variables and each causal simulation equation, and to obtain the parameter constants and parameter state variables corresponding to the target output variables;

[0015] The parameter adjustment module is used to adjust the parameter constants and parameter adjustment state variables corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value.

[0016] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned vehicle system simulation parameter adjustment method based on Modelica.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned Modelica-based vehicle system simulation parameter adjustment method.

[0018] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned Modelica-based vehicle system simulation parameter adjustment method.

[0019] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0020] The present application provides a vehicle system simulation parameter adjustment method and device based on Modelica, which obtains various causal simulation equations and vehicle simulation system output variables in the simulation solution process of a vehicle simulation system built based on Modelica; the causal simulation equations are used to describe the physical processes of various vehicle systems; the target output variable is determined based on the output variable of the vehicle simulation system; the constants and state variables that affect the target output variable in the causal simulation equations are determined based on the target output variable and each causal simulation equation, and the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable are obtained; the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable are adjusted until the target output variable reaches the expected output value. The present application searches for various parameters that affect the target output variable through the causal simulation equations in the simulation solution process, and can accurately and quickly find various parameters that affect the target output variable from a large number of parameters, thereby improving the efficiency of simulation debugging. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is an application environment diagram of a vehicle system simulation parameter adjustment method based on Modelica in one embodiment of the present application;

[0023] Figure 2 A flowchart of a vehicle system simulation parameter adjustment method based on Modelica provided in one embodiment of the present application;

[0024] Figure 3 A schematic diagram of the information interaction process between the parameter adjustment method of the present application and the simulation software kernel system provided in one embodiment of the present application;

[0025] Figure 4 A schematic diagram of a simulation calculation process based on Modelica simulation software provided in one embodiment of the present application;

[0026] Figure 5 A schematic diagram of the parameter impact and tracing process of the target output variable provided in one embodiment of the present application;

[0027] Figure 6 A schematic diagram of a parameter adjustment process based on influencing parameters of a target output variable provided in one embodiment of the present application;

[0028] Figure 7 A schematic diagram of the functional modules of a vehicle system simulation and parameter adjustment device based on Modelica provided in one embodiment of the present application;

[0029] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] The vehicle system simulation parameter adjustment method based on Modelica provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal communicates with the server via a network. The data storage system can store data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the causal simulation equations and vehicle simulation system output variables generated during the simulation and solution process of the vehicle simulation system built based on Modelica to the server. After receiving the causal simulation equations and vehicle simulation system output variables generated during the simulation and solution process of the vehicle simulation system built based on Modelica, the server determines the target output variable based on the vehicle simulation system output variable; determines the constants and state variables in the causal simulation equations that affect the target output variable based on the target output variable, and obtains the tuning constants and tuning state variables corresponding to the target output variable; and adjusts the tuning constants and tuning state variables corresponding to the target output variable to adjust the target output variable until the target output variable reaches the desired output value. The server can provide feedback to the terminal on the obtained tuning constants and tuning state variables corresponding to the target output variable. In addition, in some embodiments, the vehicle system simulation parameter adjustment method based on Modelica can also be implemented independently by a server or a terminal. For example, the terminal can directly adjust the vehicle system simulation parameters for each causal simulation equation and the vehicle simulation system output variables in the simulation solution process of the vehicle simulation system built based on Modelica, or the server can obtain the various causal simulation equations and the vehicle simulation system output variables in the simulation solution process of the vehicle simulation system built based on Modelica from the data storage system, and perform vehicle system simulation parameter adjustment.

[0033] The terminal may be, but is not limited to, various desktop computers, laptops, tablet computers, and IoT devices, and the server may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0034] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a vehicle system simulation parameter adjustment method based on Modelica is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server in is used as an example to illustrate, including the following steps 101 to 104.

[0035] Step 101 obtains the causal simulation equations used in the simulation and solution process of a vehicle simulation system built using Modelica. Causal simulation equations are used to describe the physical processes of various vehicle systems. These equations include constants, state variables of the vehicle simulation system, simulation intermediate variables, and output variables of the vehicle simulation system. State variables include vehicle speed, power battery current, air conditioner power, and air conditioner compressor power and speed. Simulation intermediate variables include various physical quantities such as vehicle acceleration, vehicle displacement, and air conditioner refrigerant temperature.

[0036] Step 102: determining a target output variable according to the output variable of the vehicle simulation system.

[0037] As an example, the target output variables include battery pack temperature and vehicle interior temperature.

[0038] Step 103, determining the constants and state variables that affect the target output variable in the causal simulation equation based on the target output variable and each causal simulation equation, and obtaining the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable.

[0039] Step 104: Adjust the parameter constants and parameter state variables corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value.

[0040] By implementing the above-mentioned steps 101 to 104, the present application can be applied to the vehicle system simulation process in the field of new energy vehicles, and to the simulation process of the system-level joint simulation system of related components such as air-conditioning systems, three-electric systems, chassis, etc., or the simulation process of a single vehicle system. By analyzing the relationship between the equations and variables between each component, the constants and state variables affecting the target output variables in the causal simulation equations are determined according to the target output variables and each causal simulation equation, and the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variables are obtained to assist modeling and simulation personnel in adjusting the structure of the model equations, solve the problem of unclear parameter adjustment, and thus improve the simulation debugging efficiency and capabilities of the new energy vehicle system simulation. Figure 3 The "Modelica model variable relationship analysis and data comparison tool" refers to a tool that applies the Modelica-based vehicle system simulation parameter adjustment method of this application.

[0041] In another exemplary embodiment of the present application, in step 101, Figure 4As shown in Figure 1, the simulation process for Modelica-based simulation software is as follows: Users use Modelica-based simulation software to graphically build large-scale model system equations (the various physical equations involved in the vehicle simulation system). The Modelica-based simulation software's core (processor) converts the user-built system equations into a causal equation structure consisting of constants, state variables, and simulation intermediate variables. State variables and simulation intermediate variables are calculated, while constants are quantities with fixed values ​​assigned during model construction. After generating the causal equations, the relevant calculation information is input into the simulation software's core's solution control and differential equation solving algorithms for calculation. The specific solution process involves integration operations based on the ODE differential equation system. Common integration algorithms include mature mathematical calculation methods such as the Euler method and the Jung-Kutta method. Finally, the simulation results are obtained. For example, in the simulation of a new energy vehicle system, the entire air conditioning system can be simulated from room temperature startup to reaching the target temperature, including the state changes of the air conditioning medium, air conditioning compressor, and other related sub-components, as well as energy consumption. The solution results are the relevant constants and state variables in the user's system model, and their time-varying relationship curves.

[0042] Therefore, after simulating the vehicle system using Modelica-based simulation software, the causal simulation equations and output variables transformed during the simulation solution process can be obtained.

[0043] The user finds the target output variable of interest in the solution results (determined manually based on the actual system simulation objectives. For example, when simulating air conditioning compressor power and speed, selecting the compressor motor requires engineers to manually define the target output variable based on engineering application needs). The target output variable is then compared with experimental data or other target results. The simulation engineer exports the target output variable results from the simulation software or loads experimental data into the model system for subtraction, then compares the data using specific differences or plots. If the target output variable results do not match expectations and require adjustment, the user can adjust the simulation parameters by adjusting the corresponding parameters or the variable's start value. In Modelica syntax, the corresponding parameters are defined as parameters, and variables have a start value attribute. The user can adjust the simulation parameters by adjusting the corresponding parameters or the variable's start value. Constants derived from the causal equation structure can be used to adjust the target output variable, and the simulation process can be repeated. However, not all constants affect the target output variable. In large-scale vehicle simulation systems, there may be thousands of constants, but only dozens of them affect the target output variable. Manual screening based on physical meaning is difficult. Therefore, in another exemplary embodiment of the present application, in step 103, the constants and state variables affecting the target output variable in the causal simulation equation are determined based on the target output variable and each causal simulation equation, specifically including:

[0044] According to the target output variable and each causal simulation equation, a recursive method is used to determine the constants and state variables that affect the target output variable in the causal simulation equation. The specific process is:

[0045] (1) Determine the causal simulation equation in which the target output variable directly participates in the calculation, which is recorded as the initial causal simulation equation.

[0046] (2) Determine the parameters required to calculate the target output variable in the initial causal simulation equation; the parameters include constants, state variables and simulation intermediate variables.

[0047] (3) Record the current constants and state variables.

[0048] (4) Determine the causal simulation equation in which the intermediate variables of the current simulation directly participate in the calculation, which is recorded as the intermediate causal simulation equation.

[0049] (5) Determine the parameters required to calculate the simulation intermediate variables in the current intermediate causal simulation equation.

[0050] (6) Return to step “record the currently obtained constants and state variables” until a stop command is received from the user or the current intermediate causal simulation equation contains only constants, and obtain all constants and all state variables that affect the target output variable.

[0051] In another exemplary embodiment of the present application, after executing step (3) "recording the currently obtained constants and state variables", the following is further included:

[0052] Analyze the causal simulation equations in the differential solution process involving state variables; determine the constants and simulation intermediate variables in the causal simulation equations in the differential solution process involving state variables.

[0053] In order to more clearly illustrate the process of analyzing the correlation between the target output variable and the constant and state variable in a recursive manner in this application, a specific example is given, such as Figure 5 shown.

[0054] 1) Once the user’s target output variables are known, the equations to be solved in the simulation software for the corresponding vehicle system can be obtained, and the variable relationships therein can be determined. Figure 5 In, described as It can be analyzed that the parameters that affect the target output variable var_4 are the constant a_3, the state variable s_1, and the simulation intermediate variable var_3.

[0055] Among them, a_3 is a constant that directly affects var_4, which is recorded, saved, and finally provided to the user;

[0056] The thick arrow on the right represents s_1 as a state variable. The actual calculation process is that the equation system first calculates its differential term der_s_1, and then gives it to the differential equation solving algorithm for integration calculation to obtain s_1 (the calculation process here is the same as Figure 4 The differential equation solution steps are mature differential equation calculation algorithms, such as the Euler method or the Jung-Kutta method, which users can choose. Whether to continue the analysis of the differential solution process of the state variables can be left to the user for confirmation, or the preset rules can be used to automatically continue to analyze the constants involved in der_s_1.

[0057] The dependencies of variables in an equation are divided into direct dependencies and indirect dependencies. Generally, direct dependency variables are provided, and the user then selects the direction for further analysis based on the variables in the direct dependency. If the user believes that the variable dependency information obtained meets the requirements, or the corresponding variable is a constant and has no dependency, the analysis will not continue.

[0058] 2) Continuing to analyze var_3, we know that var_3 is calculated from var_2 and var_1. Both are simulation intermediate variables and their values ​​cannot be adjusted to affect the target output variable, so we need to continue the analysis.

[0059] 3) Analysis shows that var_1 is calculated from s_1 and a_1. The analysis of s_1 can be handled similarly to the description in step 1). a_1 is a constant and directly affects the calculation of var_1. This in turn directly affects the result of var_4, which is recorded and ultimately provided to the user. Similarly, a_2 in var_2 also affects var_4 through propagation. This is recorded and ultimately provided to the user.

[0060] Through the above analysis process, the user will eventually be provided with the following information: constants a_1, a_2, and a_3 will affect the calculation of var_4, and the user needs to filter them; state variable s_1 will affect the calculation of var_4, and the user can choose whether to continue the analysis based on the correlation of the result output. If the user believes that the constants or initial values ​​of the state variables in the current analysis results can be used to adjust the target output variables, the analysis can be stopped without continuing the retrospective analysis; if the user believes that the currently given dependent constants or initial values ​​of the state variables cannot be adjusted, other potential variables can be selected for analysis, or if the analyzed variable is already a constant and does not depend on other variables, the analysis can no longer be continued, or the analysis can be directly performed based on the equation der_s_1.

[0061] By recursively analyzing the constants and state variables that actually influence var_4, beyond the simple form used in this example, we can obtain all the constants and state variables that ultimately influence the target output variable, providing the user with parameter adjustment suggestions. Based on the user's target output variable and the analytical operations using the recursive analysis process described above, Modelica overcomes the difficulty of analyzing correlations in non-causal system equations, and obtains the constants and state variables that the user can adjust to influence the target output variable.

[0062] The recursive analysis process is to continuously search for the dependent variables in the calculation equation corresponding to the output variable that the user needs to analyze. The purpose of the analysis is to help the user find the parameters on which the user's target output variable depends, and the parameters that the user believes can be adjusted or improved. When the user finds the parameters that he believes can be modified and then modifies them, he can quickly adjust the target output variable based on the parameters obtained from the analysis. Due to the non-causal characteristics of the Modelica language, users cannot effectively analyze causal relationships on their own. The causality of equations in the same model in different systems is different, so the influencing parameter analysis process of this application is needed to assist in variable relationship analysis.

[0063] For example, users are more concerned about the cooling capacity of the air conditioner, such as the temperature drop gradient inside the car. Through the search of the above-mentioned recursive analysis process, it can be found that certain setting parameters in the air conditioner compressor system can have an impact, and the temperature change pattern can be changed by adjusting these parameters.

[0064] In another exemplary embodiment of the present application, in step 104, adjusting the parameter constant and the parameter state variable corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value specifically includes:

[0065] According to the relationship curve of the parameter state variable over time and the parameter constant, adjust the parameter state variable and parameter constant corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value. Figure 6 shown.

[0066] This application analyzes the equation results of the simulation software model from non-causal equations to the actual simulation solution calculation process. Based on the target output variable information and variable calculation order given by the user, it skips the intermediate variables that do not affect the entire equation system through recursive analysis, and outputs the corresponding constants and state variable information that have potential impacts. It can also provide the user with the data curves of the variables and corresponding constants and state variables that need to be analyzed based on the overall results of the simulation, helping the user to analyze which potential constants and state variable initial values ​​can quickly affect the calculation of the target variables through parameter adjustment. The user can then quickly select the corresponding key influencing constants or initial values ​​of the state variables for modification and adjustment based on physical principles or relevant experience. Compared with the traditional parameter adjustment method (the user only adjusts the corresponding possible constants, initial values ​​of state variables, etc. based on the physical principles of the model and the parameters given by the model through model simulation experience, and debugs the model simulation process through continuous trial and error, the disadvantages are: since the current model equation scale is very large, for example, when the number of equations and variables in the system model reaches tens of thousands, the relationship and influence of the model variables are difficult to simply analyze and judge, and there is a large structural difference between the non-causal equations of the model and the actual solved equations. For large and complex system models, due to the large number of constants and initial values ​​of state variables, when the user is not particularly clear about the physical calculation principles of the model, there may be blindness in parameter adjustment, resulting in low efficiency of the debugging process), this application can more accurately determine the relevant parameters affecting the target output variables, avoid blindness in parameter adjustment, and thus greatly improve the efficiency of parameter adjustment.

[0067] The technical effects that this application can achieve are:

[0068] 1. Based on the complex Modelica model and target output variable established by the user, the calculation dependency order of the target output variable calculation equation and the relevant parameter information can be quickly obtained, which facilitates the user to filter and adjust the parameters based on the physical meaning, and can achieve the goal of adjusting the target output variable more quickly, saving the user time.

[0069] 2. It can obtain the data results of the user's target variables and related variables, facilitate the analysis of variable relationships, and thus support users to search and screen related variables in complex system models, and ultimately be used to locate key influencing variables in the system model and verify key components in the actual R&D process.

[0070] 3. Provide relevant analysis and tracing tools, which can connect to the equation information and solution result information related to the software kernel, realize fast and automatic acquisition of key parameter information and data comparison, and shorten the parameter calibration time of simulation experiment personnel.

[0071] The present application also provides an application scenario, which applies the above-mentioned vehicle system simulation and parameter adjustment method based on Modelica. Specifically: the vehicle system simulation and parameter adjustment method based on Modelica provided in this embodiment can be applied in new energy vehicle design scenarios. The scenario includes a vehicle performance simulation link, an influencing variable screening link, and a simulation debugging link; the vehicle performance simulation link is used to simulate and solve the vehicle simulation model built by the user to obtain simulation results; the influencing variable screening link is used to obtain the causal equations and output variables in the simulation solution process, and to screen the influencing parameters of the target output variables; the simulation debugging link is used to adjust the parameters according to the screened influencing parameters to achieve vehicle design.

[0072] Based on the same inventive concept, embodiments of the present application also provide a Modelica-based vehicle system simulation and parameter tuning device for implementing the aforementioned Modelica-based vehicle system simulation and parameter tuning method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the Modelica-based vehicle system simulation and parameter tuning device can be found in the above-mentioned limitations of the Modelica-based vehicle system simulation and parameter tuning method, and will not be further elaborated here.

[0073] In an exemplary embodiment, Figure 7 As shown, a vehicle system simulation parameter adjustment device based on Modelica is provided, comprising:

[0074] The data acquisition module M1 is used to obtain the causal simulation equations during the simulation and solution process of the vehicle simulation system built based on Modelica; the causal simulation equations are used to describe the physical processes of various vehicle systems; the causal simulation equations include constants, state variables of the vehicle simulation system, simulation intermediate variables and output variables of the vehicle simulation system.

[0075] The target variable determination module M2 is used to determine the target output variable according to the output variable of the vehicle simulation system.

[0076] The influencing variable determination module M3 is used to determine the constants and state variables that affect the target output variables in the causal simulation equations based on the target output variables and each causal simulation equation, and obtain the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variables.

[0077] The parameter adjustment module M4 is used to adjust the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value.

[0078] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, memory and input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store causal equations, output variables, target output variables and parameters affecting the target output variables in the process of solving vehicle system simulation based on Modelica. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a vehicle system simulation parameter adjustment method based on Modelica is implemented.

[0079] Those skilled in the art will understand that Figure 8The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0080] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0081] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0083] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0084] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0085] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A vehicle system simulation parameter adjustment method based on Modelica, characterized in that: include: Obtain the causal simulation equations used in the simulation and solution process of a vehicle simulation system built based on Modelica; the causal simulation equations are used to describe the physical processes of various vehicle systems; the causal simulation equations include constants, state variables of the vehicle simulation system, simulation intermediate variables, and output variables of the vehicle simulation system; Determine target output variables according to output variables of the vehicle simulation system; Determine the constants and state variables that affect the target output variables in the causal simulation equations according to the target output variables and each causal simulation equation, and obtain the parameter adjustment constants and parameter adjustment state variables corresponding to the target output variables; According to the target output variable and each causal simulation equation, the constants and state variables that affect the target output variable in the causal simulation equation are determined, specifically including: According to the target output variable and each causal simulation equation, a recursive method is used to determine the constants and state variables that affect the target output variable in the causal simulation equation; According to the target output variable and each causal simulation equation, a recursive method is used to determine the constants and state variables that affect the target output variable in the causal simulation equation, including: Determine the causal simulation equation in which the target output variable directly participates in the calculation, which is recorded as the initial causal simulation equation; Determine the parameters required to calculate the target output variable in the initial causal simulation equation; the parameters include constants, state variables and simulation intermediate variables; Record the current constants and state variables; Determine the causal simulation equation in which the current simulation intermediate variable directly participates in the calculation, which is recorded as the intermediate causal simulation equation; Determine the parameters required to calculate the simulation intermediate variables in the current intermediate causal simulation equation; Return to step "Record the currently derived constants and state variables" until a stop command is received from the user or the current intermediate causal simulation equation contains only constants, and obtain all constants and all state variables that affect the target output variable; After executing the step "Record the currently obtained constants and state variables", it also includes: Analyze the causal simulation equations in the differential solution process involving state variables; Determine the constants and simulation intermediate variables in the causal simulation equations in the differential solution process involving state variables; Adjust the parameter constant and parameter state variable corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value; Users use Modelica-based simulation software to build large-scale model system equations through graphical modeling. The core of the Modelica-based simulation software will convert the system equations built by the user into a causal equation structure. After generating the causal equations, the relevant calculation information is input into the simulation software core's solution control and differential equation solving algorithm for calculation to obtain the simulation solution results.

2. The vehicle system simulation parameter adjustment method based on Modelica according to claim 1, characterized in that: State variables include vehicle speed, power battery current, air conditioning power, and air conditioning compressor power and speed; The target output variables include battery pack temperature and vehicle interior temperature.

3. The vehicle system simulation parameter adjustment method based on Modelica according to claim 1, characterized in that: Adjust the parameter constants and parameter state variables corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value, specifically including: According to the relationship curve of the parameter state variable over time and the parameter constant, the parameter state variable and the parameter constant corresponding to the target output variable are adjusted respectively to adjust the target output variable until the target output variable reaches the expected output value.

4. A vehicle system simulation and parameter adjustment device based on Modelica, characterized in that: The vehicle system simulation parameter adjustment method based on Modelica according to any one of claims 1 to 3 is operated, comprising: The data acquisition module is used to obtain the causal simulation equations used in the simulation and solution process of the vehicle simulation system built based on Modelica. The causal simulation equations are used to describe the physical processes of various vehicle systems. The causal simulation equations include constants, state variables of the vehicle simulation system, simulation intermediate variables, and output variables of the vehicle simulation system. A target variable determination module, used for determining a target output variable according to an output variable of a vehicle simulation system; An influencing variable determination module is used to determine the constants and state variables that affect the target output variables in the causal simulation equations based on the target output variables and each causal simulation equation, and to obtain the parameter constants and parameter state variables corresponding to the target output variables; The parameter adjustment module is used to adjust the parameter constants and parameter adjustment state variables corresponding to the target output variable to adjust the target output variable until the target output variable reaches the expected output value.

5. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the Modelica-based vehicle system simulation parameter adjustment method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the vehicle system simulation parameter adjustment method based on Modelica according to any one of claims 1 to 3 is implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the vehicle system simulation parameter adjustment method based on Modelica according to any one of claims 1 to 3 is implemented.

Images