A method, apparatus, medium and equipment for simulating the driving range of new energy vehicles

Abstract

This invention belongs to the field of automotive thermal management technology and provides a method, device, medium and equipment for simulating the driving range of new energy vehicles. It includes: splitting the thermal management system and developing several sub-models such as air conditioning circuit, electric drive water circuit, battery water circuit and passenger compartment; establishing training datasets for each thermal management system subsystem based on one-dimensional thermal management simulation; using machine learning methods to train the training datasets to obtain reduced-order models of each thermal management system subsystem; and integrating the reduced-order sub-models and management control strategies into the whole vehicle dynamics model to achieve efficient simulation of high and low temperature driving range.

Description

Technical Field

[0001] This invention belongs to the field of automotive thermal management technology, and in particular relates to a method, device, medium and equipment for simulating the driving range of new energy vehicles. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In summer cooling and winter heating conditions, the thermal management system of new energy vehicles consumes a large amount of electrical energy, resulting in a significant reduction in actual driving range and hindering the market competitiveness of new energy vehicles. To improve the actual driving range of pure electric vehicles under high and low temperature conditions, optimizing the energy consumption of the thermal management system based on high and low temperature driving range simulation results during the development phase is a widely adopted technical approach by OEMs.

[0004] At present, high and low temperature range simulation needs to couple the vehicle dynamics model and the thermal management system model. Due to the slow convergence of the thermal management system model during the simulation process, the high and low temperature range simulation cycle is very long. In the context of the industry where the speed of vehicle development is rapidly increasing, how to improve the efficiency of high and low temperature range simulation has become a problem. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a method, apparatus, medium, and equipment for simulating the driving range of new energy vehicles. The thermal management system is broken down into several sub-models, including an air conditioning circuit, an electric drive water circuit, a battery water circuit, and a passenger compartment. Training datasets for each thermal management system subsystem are established based on one-dimensional thermal management simulation. Machine learning methods are used to train the datasets to obtain reduced-order models for each thermal management system subsystem. These reduced-order sub-models and management control strategies are integrated into the overall vehicle dynamics model, achieving efficient simulation of driving range at high and low temperatures.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of this invention provides a method for simulating the driving range of new energy vehicles.

[0008] A method for simulating the driving range of new energy vehicles includes:

[0009] Obtain the parameters of the controlled component and the vehicle parameters at several consecutive time points as a sequence of input variables;

[0010] Based on the input variable sequence, the output variables are predicted through various reduced-order sub-models; based on the output variables of each reduced-order sub-model, the load power and system state parameters are obtained; based on the load power, the vehicle parameters are calculated through the vehicle dynamics model; based on the system state parameters, the parameters of the controlled components are calculated through the management and control strategy.

[0011] Determine if the battery is depleted. If so, calculate the remaining driving range. Otherwise, use the calculated parameters of the controlled component and the vehicle parameters as the latest data, add them to the input variable sequence, and return the predicted output variable.

[0012] The training process of the reduced-order sub-model includes: splitting the vehicle thermal management system model into an air conditioning system sub-model, a motor system cooling circuit sub-model, a battery system cooling circuit sub-model, and a passenger compartment model; generating several input variable sequences for each sub-model obtained from the splitting, and obtaining output variables through simulation; matching the input variable sequences with the output variables one by one to obtain a training dataset; and using the training dataset to train the neural network to obtain the reduced-order sub-model.

[0013] Furthermore, for the air conditioning system sub-model, the input variables include compressor speed, condenser inlet air temperature, condenser inlet air volume, evaporator inlet air humidity, evaporator inlet air volume, evaporator inlet air temperature, electronic expansion valve superheat, thermal expansion valve superheat, inlet water temperature and inlet water flow of the inlet plate heat exchanger of the open-loop battery circuit.

[0014] Alternatively, for the air conditioning system sub-model, the output variables include compressor power, compressor discharge temperature, blower power, fan power, system refrigerant high pressure, system refrigerant low pressure, HVAC outlet air temperature, plate heat exchanger refrigerant outlet temperature and outlet pressure, and battery inlet water temperature.

[0015] Furthermore, for the motor system cooling circuit sub-model, the input variables include water pump speed, radiator inlet air velocity, radiator inlet air temperature, motor speed, motor torque, and electronic control heat.

[0016] Alternatively, for the sub-model of the motor system cooling circuit, the main output variables are coolant flow rate, radiator inlet water temperature, motor controller inlet water temperature, motor body temperature, and electrical power of each component of the motor system.

[0017] Furthermore, for the battery system cooling circuit sub-model, the input variables include battery inlet water temperature, electric water pump speed, battery charge, current, voltage, and battery initial temperature;

[0018] Alternatively, for the battery system cooling circuit sub-model, the output variables include coolant flow rate, battery inlet / outlet water temperature, maximum cell temperature, minimum cell temperature, and battery heat generation.

[0019] Furthermore, for the passenger cabin model, the input variables include the air temperature and air volume at the air conditioning unit outlet, the ambient temperature, the solar radiation intensity, the vehicle speed, and the initial temperature of the cabin air.

[0020] Alternatively, for the crew cabin model, the output variables include the average cabin air temperature and the blower power.

[0021] Furthermore, the step of training the neural network using the training dataset includes:

[0022] Initialize a population containing several individuals, each of which encodes the number of hidden layers and initial learnable parameters of a neural network;

[0023] For each individual's corresponding neural network, a training set is used for training to optimize the learnable parameters. After training, the error value is obtained.

[0024] The error value is used as a fitness function for population selection and genetic operations;

[0025] Determine if the number of iterations has reached the maximum number of iterations. If so, use the neural network trained based on the optimal individual as the reduced-order sub-model; otherwise, increment the number of iterations and return to train the neural network corresponding to each individual.

[0026] Furthermore, for the reduced-order sub-model, the number of time points in the input variable sequence used for output variable prediction is consistent with the number of hidden layers corresponding to the optimal individual.

[0027] A second aspect of the present invention provides a simulation device for the driving range of new energy vehicles.

[0028] A new energy vehicle range simulation device includes:

[0029] The reduced-order sub-model generation module is configured to: decompose the vehicle thermal management system model into an air conditioning system sub-model, a motor system cooling circuit sub-model, a battery system cooling circuit sub-model, and a passenger compartment model; generate several input variable sequences for each sub-model obtained from the decomposition, and obtain output variables through simulation; match the input variable sequences with the output variables one by one to obtain a training dataset; use the training dataset to train the neural network to obtain the reduced-order sub-model.

[0030] The data acquisition module is configured to acquire parameters of the controlled component and vehicle parameters at several consecutive time points as a sequence of input variables.

[0031] The prediction module is configured to: predict the output variables based on the input variable sequence through various reduced-order sub-models; obtain the load power and system state parameters based on the output variables of each reduced-order sub-model; calculate the vehicle parameters based on the load power through the vehicle dynamics model; and calculate the parameters of the controlled components based on the system state parameters through management and control strategies.

[0032] The judgment module is configured to: determine whether the battery is depleted; if so, calculate the driving range; otherwise, use the calculated parameters of the controlled component and the vehicle parameters as the latest data, add them to the input variable sequence, and return to the prediction module.

[0033] A third aspect of the present invention provides a computer-readable storage medium.

[0034] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the new energy vehicle range simulation method described in the first aspect above.

[0035] A fourth aspect of the present invention provides a computer device.

[0036] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the new energy vehicle range simulation method described in the first aspect above.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] This invention breaks down the thermal management system into several sub-models, including the air conditioning circuit, the electric drive water circuit, the battery water circuit, and the passenger compartment. Based on one-dimensional thermal management simulation, training datasets for each thermal management system subsystem are established. Machine learning methods are used to train the datasets to obtain reduced-order models for each thermal management system subsystem. The reduced-order sub-models and management control strategies are integrated into the vehicle dynamics model, achieving efficient simulation of high and low temperature range driving. Compared with conventional methods, the simulation time can be shortened from 5 hours to 5 minutes, significantly reducing the simulation time for high and low temperature range driving.

[0039] This invention breaks down the thermal management system and develops reduced-order models for the air conditioning circuit, motor system water circuit, battery water circuit, and passenger compartment, thereby improving the feasibility of engineering development and the accuracy of the model.

[0040] This invention breaks down the thermal management system and develops reduced-order sub-models for the air conditioning circuit, electric drive water circuit, battery water circuit, and passenger compartment, thereby promoting the engineering application value of reduced-order models in the field of vehicle thermal management simulation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0042] Figure 1 This is a flowchart illustrating a method for simulating the driving range of a new energy vehicle, as shown in Embodiment 1 of the present invention.

[0043] Figure 2 This is an example diagram of a sub-model of an air conditioning system shown in Embodiment 1 of the present invention;

[0044] Figure 3 This is a flowchart illustrating the process of establishing a sub-model training dataset as shown in Embodiment 1 of the present invention;

[0045] Figure 4 This is a data transmission example diagram illustrating the thermal management model, vehicle dynamics model, and thermal management control strategy shown in Embodiment 1 of the present invention;

[0046] Figure 5 This is a schematic diagram of the structure of a computer device shown in Embodiment 4 of the present invention. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0049] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0050] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and apparatuses according to various embodiments of the present invention. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using dedicated hardware-based apparatus that performs the specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions.

[0051] Example 1

[0052] This embodiment provides a method for simulating the driving range of new energy vehicles.

[0053] This embodiment provides a method for simulating the driving range of new energy vehicles. It uses machine learning to develop a reduced-order model of the thermal management system, and establishes an engineering-based method for simulating the driving range of new energy vehicles based on the reduced-order model. This method can significantly improve the efficiency of high and low temperature driving range simulation, so as to meet the industry demand for rapid vehicle development and iteration.

[0054] This embodiment provides a method for simulating the driving range of new energy vehicles, such as... Figure 1 As shown, it includes the following steps:

[0055] S1: The vehicle thermal management system model is divided into four sub-models: air conditioning system, motor system cooling circuit, battery system cooling circuit, and passenger compartment, and data transmission interfaces between the sub-models are set.

[0056] For the data transmission interface between sub-models, the relevant sub-model interaction parameters mainly include water flow rate, inlet and outlet water temperature, air volume, and air temperature. The specific parameters depend on the model.

[0057] The air conditioning system mainly includes an electric compressor 1, a blower 2, a condenser 5, an evaporator 3, a thermal expansion valve 4, an electronic expansion valve 6, a plate heat exchanger 7, an air conditioning circuit 8, and an open-loop battery water circuit 9, such as... Figure 2 As shown.

[0058] For the air conditioning system sub-model, the main input variables include compressor speed, condenser inlet air temperature, condenser inlet air volume, evaporator inlet air humidity, evaporator inlet air volume, evaporator inlet air temperature, electronic expansion valve superheat, thermal expansion valve superheat, inlet water temperature and inlet water flow of the inlet plate heat exchanger in the open-loop battery circuit, etc.

[0059] For the air conditioning system sub-model, the predicted variables mainly include compressor power, compressor exhaust temperature, blower power, fan power, system refrigerant high pressure, system refrigerant low pressure, HVAC outlet air temperature, plate heat exchanger refrigerant outlet temperature and outlet pressure, and battery inlet water temperature.

[0060] The sub-model of the motor system cooling loop mainly includes an electric water pump, a motor, a motor controller, and a radiator. The main input variables are water pump speed, radiator air intake speed, radiator air intake temperature, motor speed, motor torque, and electrical control heat. The main prediction variables are coolant flow rate, radiator inlet water temperature, motor controller inlet water temperature, motor body temperature, and electrical power of each component of the motor system.

[0061] The battery system cooling circuit sub-model mainly includes an electronic water pump and a battery. The main input variables are battery inlet water temperature, electronic water pump speed, battery charge, current, voltage, and battery initial temperature. The main output variables are coolant flow rate, battery inlet / outlet water temperature, maximum cell temperature, minimum cell temperature, and battery heat generation.

[0062] For the passenger compartment model, which mainly includes air ducts and passenger compartment, the main input variables are air temperature and air volume at the air conditioning unit outlet, ambient temperature, solar radiation intensity, vehicle speed, and initial air temperature inside the compartment; the main output variables are average air temperature inside the compartment and blower power.

[0063] S2: Determine the range of input variable parameters and establish a training dataset for the four subsystem models of the thermal management system based on one-dimensional simulation software.

[0064] When determining the range of input variables, it is essential to cover the actual simulation conditions to avoid extrapolation that could reduce model accuracy. At the same time, the range of input variables should not be too large to avoid increasing the amount of data and training time.

[0065] Based on the input variables of each subsystem identified above, and by determining the range of values ​​for the input variables based on experience.

[0066] based on Figure 3The technical approach shown establishes a training dataset in the following ways: First, several random numbers are generated within the range of input variable values. The number of random numbers determines the amount of data in the training set, typically exceeding 10,000. Then, based on the coupling of MATLAB Simulink with one-dimensional thermal management simulation software (such as KULI, AMESim, and GT), simulations of each sub-model are conducted. During the simulation, the input variables are controlled to change over time to obtain simulation results. That is, the input of each sub-model is a sequence that changes over time. Finally, based on the results from the one-dimensional simulation software, the outputs under different input variable sequences are obtained, and after processing, the training dataset for the corresponding sub-model is obtained.

[0067] Specifically, automated scripts are developed to extract the input variable sequence and the values ​​of the output variables of interest from the simulation result file. The input variable sequence consists of input parameters designed for several consecutive time points, while the output variables are determined according to the development requirements of the reduced-order model. Generally, the output variables of the air conditioning system include parameters such as compressor power, compressor exhaust temperature, blower power, fan power, system refrigerant high pressure, system refrigerant low pressure, HVAC outlet air temperature, plate heat exchanger refrigerant outlet temperature and outlet pressure, and battery inlet water temperature; the output variables of the motor system cooling circuit include coolant flow rate, radiator inlet water temperature, motor controller inlet water temperature, motor body temperature, and electrical power of various components of the motor system; the output variables of the battery system cooling circuit mainly include coolant flow rate, battery inlet / outlet water temperature, maximum cell temperature, minimum cell temperature, and battery heat generation.

[0068] The input and output variables generated by the automated script are arranged in order in an Excel file, serving as the training dataset.

[0069] S3: Select an appropriate algorithm to train the training dataset and obtain the reduced-order models of the four subsystems of the thermal management system.

[0070] Among them, the cooling circuits of the air conditioning system and motor system have relatively small system heat capacity, so the influence of the time domain on the model is small and the influence of the transient stage can be ignored. A steady-state scheme can be used to establish a reduced-order model.

[0071] Among them, the battery system cooling circuit and the passenger compartment model have large heat capacities, and the transient characteristics of the model cannot be ignored. A transient approach is needed to establish a reduced-order model.

[0072] In this embodiment, the development of reduced-order models for each subsystem can be accomplished using the commercial software Altair ROMAI, which can train both steady-state and transient models. Alternatively, the NSS (neural state-space model) model can also be used to train the reduced-order model.

[0073] Preferably, the reduced-order model for each subsystem can employ an LSTM network. As mentioned above, different sub-models are affected by the time domain to varying degrees. This embodiment proposes using a genetic algorithm to optimize the number of hidden layers in different sub-models to obtain the reduced-order sub-models. Specifically:

[0074] (1) For a certain sub-model, initialize the population and the number of iterations. The population contains several individuals, and each individual encodes the number of hidden layers and initial learnable parameters of an LSTM network.

[0075] (2) For each individual, the LSTM network is trained using the training set to optimize the learnable parameters. After training, the error value of the LSTM network for each individual is obtained.

[0076] (3) Use the error value as the fitness function to perform population screening and genetic operations;

[0077] (4) Determine whether the number of iterations has reached the maximum number of iterations. If so, use the LSTM network trained based on the optimal individual as the reduced-order sub-model; otherwise, increment the number of iterations by 1 and return to step (2).

[0078] S4: Integrate the reduced-order models of the four subsystems of the integrated thermal management system and the vehicle dynamics model, establish an integrated visualization simulation platform, and conduct high and low temperature driving range simulations for new energy vehicles.

[0079] In the S4 stage, based on an integrated platform (such as MATLAB Simulink), four reduced-order sub-models of the thermal management system, the vehicle dynamics model, and the vehicle thermal management system control strategy are integrated to establish a simulation model of the high and low temperature driving range of new energy vehicles based on the reduced-order model.

[0080] Specifically, the trained reduced-order sub-models are output in the form of FMUs. The reduced-order sub-models are called through the Simulink FMU module. Based on Simulink, the reduced-order sub-models are integrated with the vehicle thermal management system control strategy and the vehicle dynamics model. Simulation of the high and low temperature driving range of new energy vehicles is carried out.

[0081] When conducting range simulation, the data transfer between the thermal management system, the vehicle dynamics model, and the control strategy, such as... Figure 4 As shown, when the battery power is depleted, the driving range of the vehicle at that moment is obtained, which is used as the overall driving range of the vehicle under high / low temperature conditions.

[0082] During the model integration process, it is necessary to properly set up the data transmission interfaces between various subsystems and between the subsystems and the vehicle dynamics model.

[0083] Specifically, the steps of the battery life simulation include:

[0084] (1) Obtain the parameters of the controlled component and the vehicle parameters (vehicle speed, heat generation, etc.) at several consecutive time points as the input variable sequence, and obtain the output variable through various reduced-order sub-models:

[0085] (2) Based on the output variables of each reduced-order sub-model, the load power and system state parameters are obtained; based on the load power, the vehicle parameters (vehicle speed, heat generation, etc.) are obtained through the vehicle dynamics model; based on the system state parameters, the parameters of the controlled components are obtained through the management and control strategy.

[0086] If the reduced-order sub-model uses a neural network LSTM, the input variable sequence is cut based on the number of hidden layers obtained by optimization. That is, if the number of hidden layers is n, only the input variables at the latest n time steps are retained in the input variable sequence.

[0087] (3) Determine whether the battery is depleted. If so, obtain the driving range of the vehicle model by reading the result file of the simulation model, and use it as the driving range of the vehicle under high / low temperature conditions. Otherwise, take the controlled component parameters and vehicle parameters obtained in step (2) as the latest data, add them to the input variable sequence, obtain the output variables through each reduced-order sub-model, and return to step (2).

[0088] This embodiment provides a method for simulating the driving range of new energy vehicles. The thermal management system is divided into several sub-models, such as the air conditioning circuit, the electric drive water circuit, the battery water circuit, and the passenger compartment. Training datasets for each thermal management system subsystem are established based on one-dimensional thermal management simulation. Machine learning methods are used to train the datasets to obtain reduced-order models of each thermal management system subsystem. The reduced-order models are integrated into the dynamic model to achieve efficient simulation of driving range at high and low temperatures.

[0089] This embodiment provides a method for simulating the driving range of new energy vehicles, which can efficiently conduct driving range simulation. Compared with conventional methods, it can shorten the simulation time from 5 hours to 5 minutes, significantly reducing the simulation time for high and low temperature driving range, and realizing the rapid and efficient optimization of the vehicle's driving range and energy consumption.

[0090] This embodiment provides a method for simulating the driving range of new energy vehicles. It breaks down the thermal management system and develops reduced-order models for the air conditioning circuit, motor system water circuit, battery water circuit, and passenger compartment, thereby improving the engineering feasibility and model accuracy of the thermal management system.

[0091] This embodiment provides a method for simulating the driving range of new energy vehicles. It decomposes the thermal management system and develops reduced-order sub-models such as the air conditioning circuit, electric drive water circuit, battery water circuit, and passenger compartment, thereby promoting the engineering application value of reduced-order models in the field of vehicle thermal management simulation.

[0092] This embodiment provides a method for simulating the driving range of new energy vehicles. It integrates the developed thermal management system subsystem reduced-order model into the dynamic simulation model and establishes an integrated visualization simulation platform, thereby realizing efficient simulation of the driving range of new energy vehicles at high and low temperatures.

[0093] This embodiment provides a method for simulating the driving range of new energy vehicles. Based on the established thermal management simulation reduced-order model database, it promotes the feasibility of expanding the application of reduced-order models to models on the same platform.

[0094] Example 2

[0095] This embodiment provides a simulation device for the driving range of new energy vehicles.

[0096] A new energy vehicle range simulation device includes:

[0097] The reduced-order sub-model generation module is configured to: decompose the vehicle thermal management system model into an air conditioning system sub-model, a motor system cooling circuit sub-model, a battery system cooling circuit sub-model, and a passenger compartment model; generate several input variable sequences for each sub-model obtained from the decomposition, and obtain output variables through simulation; match the input variable sequences with the output variables one by one to obtain a training dataset; use the training dataset to train the neural network to obtain the reduced-order sub-model.

[0098] The data acquisition module is configured to acquire parameters of the controlled component and vehicle parameters at several consecutive time points as a sequence of input variables.

[0099] The prediction module is configured to: predict the output variables based on the input variable sequence through various reduced-order sub-models; obtain the load power and system state parameters based on the output variables of each reduced-order sub-model; calculate the vehicle parameters based on the load power through the vehicle dynamics model; and calculate the parameters of the controlled components based on the system state parameters through management and control strategies.

[0100] The judgment module is configured to: determine whether the battery is depleted; if so, calculate the driving range; otherwise, use the calculated parameters of the controlled component and the vehicle parameters as the latest data, add them to the input variable sequence, and return to the prediction module.

[0101] It should be noted that the new energy vehicle range simulation device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the electronic device can be divided into different functional modules to complete all or part of the functions described above. In addition, the new energy vehicle range simulation device and the new energy vehicle range simulation method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0102] Example 3

[0103] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the new energy vehicle range simulation method described in Embodiment 1 above.

[0104] Example 4

[0105] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the new energy vehicle range simulation method described in Embodiment 1 above.

[0106] Figure 5 A schematic diagram of the structure of a computer device provided in an exemplary embodiment of this application is shown. The computer device includes a processor and a memory.

[0107] The processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field Programmable Gate Array), or PLA (Programmable Logic Array). The processor may also include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), 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 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0108] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory 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 some embodiments, the non-transitory computer-readable storage media in the memory are used to store at least one computer program, which is configured by a processor to implement the in-vehicle occupant detection method provided in the method embodiments of this application.

[0109] Those skilled in the art will understand that the structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or employ different component arrangements.

[0110] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for simulating the driving range of new energy vehicles, characterized in that, include: Obtain the parameters of the controlled component and the vehicle parameters at several consecutive time points as a sequence of input variables; Based on the input variable sequence, the output variables are predicted through various reduced-order sub-models; based on the output variables of each reduced-order sub-model, the load power and system state parameters are obtained; based on the load power, the vehicle parameters are calculated through the vehicle dynamics model. Based on system state parameters, the parameters of the controlled components are calculated through management and control strategies. Determine if the battery is depleted; if so, calculate the remaining driving range. Otherwise, the calculated parameters of the controlled component and the vehicle parameters are used as the latest data, added to the input variable sequence, and the predicted output variable is returned. The training process of the reduced-order sub-model includes: splitting the vehicle thermal management system model into an air conditioning system sub-model, a motor system cooling circuit sub-model, a battery system cooling circuit sub-model, and a passenger compartment model; generating several input variable sequences for each sub-model obtained from the splitting, and obtaining output variables through simulation; matching the input variable sequences with the output variables one by one to obtain a training dataset; and using the training dataset to train the neural network to obtain the reduced-order sub-model.

2. The method for simulating the driving range of a new energy vehicle according to claim 1, characterized in that, For the air conditioning system sub-model, the input variables include compressor speed, condenser inlet air temperature, condenser inlet air volume, evaporator inlet air humidity, evaporator inlet air volume, evaporator inlet air temperature, electronic expansion valve superheat, thermal expansion valve superheat, and inlet water temperature and flow rate of the inlet plate heat exchanger of the open-loop battery circuit. Alternatively, for the air conditioning system sub-model, the output variables include compressor power, compressor discharge temperature, blower power, fan power, system refrigerant high pressure, system refrigerant low pressure, HVAC outlet air temperature, plate heat exchanger refrigerant outlet temperature and outlet pressure, and battery inlet water temperature.

3. The method for simulating the driving range of a new energy vehicle according to claim 1, characterized in that, For the motor system cooling circuit sub-model, the input variables include water pump speed, radiator inlet air velocity, radiator inlet air temperature, motor speed, motor torque, and electronic control heat. Alternatively, for the sub-model of the motor system cooling circuit, the main output variables are coolant flow rate, radiator inlet water temperature, motor controller inlet water temperature, motor body temperature, and electrical power of each component of the motor system.

4. The method for simulating the driving range of a new energy vehicle according to claim 1, characterized in that, For the battery system cooling circuit sub-model, the input variables include battery inlet water temperature, electric water pump speed, battery charge, current, voltage, and battery initial temperature. Alternatively, for the battery system cooling circuit sub-model, the output variables include coolant flow rate, battery inlet / outlet water temperature, maximum cell temperature, minimum cell temperature, and battery heat generation.

5. The method for simulating the driving range of a new energy vehicle according to claim 1, characterized in that, For the passenger cabin model, the input variables include the air temperature and air volume at the air conditioning unit outlet, the ambient temperature, the solar radiation intensity, the vehicle speed, and the initial temperature of the air inside the cabin. Alternatively, for the crew cabin model, the output variables include the average cabin air temperature and the blower power.

6. The method for simulating the driving range of a new energy vehicle according to claim 1, characterized in that, The step of training the neural network using a training dataset includes: Initialize a population containing several individuals, each of which encodes the number of hidden layers and initial learnable parameters of a neural network; For each individual's corresponding neural network, a training set is used for training to optimize the learnable parameters. After training, the error value is obtained. The error value is used as a fitness function for population selection and genetic operations; Determine if the number of iterations has reached the maximum number of iterations. If so, use the neural network trained based on the optimal individual as the reduced-order sub-model; otherwise, increment the number of iterations and return to train the neural network corresponding to each individual.

7. The method for simulating the driving range of a new energy vehicle according to claim 6, characterized in that, For the reduced-order sub-model, the number of time points in the input variable sequence used for output variable prediction is consistent with the number of hidden layers corresponding to the optimal individual.

8. A simulation device for the driving range of a new energy vehicle, characterized in that, include: The reduced-order sub-model generation module is configured to: decompose the vehicle thermal management system model into an air conditioning system sub-model, a motor system cooling circuit sub-model, a battery system cooling circuit sub-model, and a passenger compartment model; generate several input variable sequences for each sub-model obtained from the decomposition, and obtain output variables through simulation; match the input variable sequences with the output variables one by one to obtain a training dataset; use the training dataset to train the neural network to obtain the reduced-order sub-model. The data acquisition module is configured to acquire parameters of the controlled component and vehicle parameters at several consecutive time points as a sequence of input variables. The prediction module is configured to: predict the output variables based on the input variable sequence through various reduced-order sub-models; obtain the load power and system state parameters based on the output variables of each reduced-order sub-model; and calculate the vehicle parameters based on the load power through the vehicle dynamics model. Based on system state parameters, the parameters of the controlled components are calculated through management and control strategies. The judgment module is configured to: determine whether the battery is depleted; if so, calculate the remaining driving range. Otherwise, the calculated parameters of the controlled component and the vehicle parameters are used as the latest data, added to the input variable sequence, and returned to the prediction module.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the new energy vehicle range simulation method as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the new energy vehicle range simulation method as described in any one of claims 1-7.

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