Virtual fuel vehicle power assembly mapping method and system

Abstract

The invention discloses a virtual fuel vehicle power assembly mapping method and system. The method comprises the steps that the vehicle speed of an electric vehicle and input signals of an accelerator pedal and a brake pedal are received; performing nonlinear mapping on the accelerator pedal input signal to generate a virtual accelerator opening degree, and performing cooperative control in combination with a brake pedal signal; based on the vehicle speed and the virtual accelerator opening degree, a virtual target gear is determined through a multi-dimensional gear shifting diagram and a self-adaptive gear shifting strategy; the method is characterized in that a dynamic slip frequency factor related to the vehicle speed, the virtual accelerator opening degree and the brake pedal opening degree is introduced in the non-gear-shifting state so as to simulate the slip frequency effect of a torque converter; in the gear shifting state, transient smooth transition of the rotating speed is simulated. According to the method, the nonlinear dynamic behavior of the fuel vehicle can be accurately simulated in real time with low calculation overhead, the authenticity and immersion of simulation are remarkably improved, and powerful support is provided for virtual testing, control system development and man-machine interaction application.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of vehicle simulation, control and human-computer interaction, and particularly relates to a virtual fuel vehicle powertrain mapping method and system. BACKGROUND

[0002] With the transformation of the automotive industry towards electrification and intelligence, higher requirements are put forward for simulation testing technology in the vehicle development process. In many scenarios, such as driving simulators, hardware-in-the-loop (HIL) testing, algorithm verification of advanced driver assistance systems (ADAS), etc., it is necessary to convert the operation input of one type of vehicle (such as an electric vehicle) into the dynamic response of another type of vehicle (such as a traditional fuel vehicle). The core challenge of this cross-type mapping is how to balance the authenticity, computational efficiency and real-time performance of the simulation.

[0003] The prior art mainly realizes this mapping in two ways, the first of which is simple linear mapping or lookup table method, which directly maps the accelerator pedal opening of an electric vehicle to the throttle opening of a fuel vehicle through linear scaling or a two-dimensional lookup table, and determines the shifting time based on a simple vehicle speed threshold. This method has small calculation amount and good real-time performance, but it seriously ignores the nonlinear characteristics of the fuel vehicle powertrain. For example, the torque output curve of the engine at different speeds and loads, the slip effect of the torque converter at start-up, the complex adaptive shifting strategy of the gearbox, etc. cannot be accurately simulated, resulting in distorted simulation results and harsh driving experience, which is difficult to meet the needs of high-fidelity simulation and precise control system development. The second is simulation based on physical models, which establishes detailed physical models for engine, gearbox and other components, and simulates their behavior by solving complex dynamic equations. Although it can achieve high authenticity in theory, it has huge calculation amount and is difficult to meet the real-time simulation requirements. In addition, accurate calibration of model parameters requires a large amount of experimental data, which is time-consuming and laborious, and the interface and mapping mechanism of the electric vehicle input signal as a driving signal for complex physical models also face challenges in authenticity and efficiency.

[0004] In summary, the prior art has significant shortcomings in mapping accuracy, authenticity and real-time performance. Especially when it is necessary to convert electric vehicle operations efficiently, in real time and accurately into the response of a complex fuel vehicle powertrain, the existing methods cannot effectively capture the inherent nonlinear characteristics and dynamic behavior of the fuel vehicle, resulting in limited authenticity of the simulation results, thereby prolonging the development cycle and increasing costs. SUMMARY

[0005] The present application aims to overcome the above-mentioned shortcomings of the prior art and provide a virtual fuel vehicle powertrain mapping method and system based on electric vehicle input signals, which aims to solve the contradiction between authenticity, efficiency and real-time performance in cross-type vehicle powertrain simulation.

[0006] In order to achieve the above object, a virtual fuel vehicle powertrain mapping method is provided in an embodiment of the present application, which comprises the following steps: Step 1, receiving real-time input signals of an electric vehicle, wherein the input signals at least include vehicle speed information, accelerator pedal input information and brake pedal input information; Step 2, generating a virtual throttle opening degree through a preset nonlinear mapping relationship according to the accelerator pedal input information; Step 3, determining a target gear position of a virtual powertrain according to the vehicle speed information and the virtual throttle opening degree in combination with a preset gear shifting strategy; Step 4, calculating a simulated engine speed, wherein the calculation step comprises: in a non-gear shifting state, calculating a theoretical engine speed based on the vehicle speed information, a current gear position and a transmission ratio, and introducing a dynamic slip factor related to the vehicle speed information, the virtual throttle opening degree and the brake pedal input information to adjust the theoretical engine speed so as to simulate the slip effect of a torque converter or a clutch; in a gear shifting state, calculating and generating a smooth speed change curve in a gear shifting process through a preset transient speed model according to a current speed before gear shifting and a target speed after gear shifting.

[0007] In one or more embodiments of the present application, in the step 2, the nonlinear mapping relationship can be adjusted based on a configurable driving style parameter, wherein the driving style parameter at least includes an economic mode, a standard mode and a sports mode, and different modes correspond to different nonlinear mapping curves.

[0008] In one or more embodiments of the present application, the step 2 further comprises: when it is detected that the brake pedal input information is valid, forcibly setting the virtual throttle opening degree to a preset low value or zero.

[0009] In one or more embodiments of the present application, in the step 3, the gear shifting strategy is implemented based on a multi-dimensional gear shifting map, wherein the gear shifting map defines upshift speed threshold values and downshift speed threshold values between gears at different virtual throttle opening degrees; and the gear shifting strategy further comprises a forced downshift logic triggered when it is detected that the virtual throttle opening degree is sharply increased in a short time.

[0010] In one or more embodiments of the present application, the multi-dimensional gear shifting map is generated through machine learning model training on actual fuel vehicle data, wherein the machine learning model includes a decision tree, a random forest, a support vector machine or a neural network.

[0011] In one or more embodiments of the present application, in the step 4, the transient speed model in the gear shifting state comprises: In an upshift process, an S-curve or a smooth step function is used to make the simulated engine speed smoothly decrease from a current speed to a target speed; In the downshift process, the overshoot model or the rotational speed matching model is used to quickly raise the simulated engine rotational speed from the current rotational speed to the target rotational speed.

[0012] In one or more embodiments of the present application, the method further comprises gear state management, which comprises managing the running, upshift, downshift and parking / neutral states of the virtual powertrain based on the vehicle speed information and the virtual throttle opening, and avoiding frequent gear shifting in critical working conditions through a soft threshold or a probability inference mechanism.

[0013] In another aspect of the present application, a virtual fuel vehicle powertrain mapping system is provided for the virtual fuel vehicle powertrain mapping method described above, the virtual fuel vehicle powertrain mapping system comprising: An input signal receiving module for receiving real-time input signals of an electric vehicle, the input signals at least including vehicle speed information, accelerator pedal input information and brake pedal input information; A virtual throttle mapping module connected with the input signal receiving module, for generating a virtual throttle opening through a preset nonlinear mapping relationship according to the accelerator pedal input information; A target gear determination module connected with the input signal receiving module and the virtual throttle mapping module, for determining the target gear of the virtual powertrain according to the vehicle speed information and the virtual throttle opening in combination with a preset gear shifting strategy; A simulated engine rotational speed calculation module connected with the input signal receiving module, the virtual throttle mapping module and the target gear determination module, configured to: In a non-gear shifting state, the theoretical engine rotational speed is calculated based on the vehicle speed information, the current gear and the transmission ratio, and a dynamic slip factor related to the vehicle speed information, the virtual throttle opening and the brake pedal input information is introduced to adjust the theoretical engine rotational speed to simulate the slip effect of the torque converter or the clutch; In a gear shifting state, the smooth rotational speed change curve in the gear shifting process is calculated and generated through a preset transient rotational speed model according to the current rotational speed before gear shifting and the target rotational speed after gear shifting.

[0014] In one or more embodiments of the present application, the virtual throttle mapping module is further configured to adjust the nonlinear mapping relationship according to a configurable driving style parameter, and to forcibly set the virtual throttle opening to a preset low value or zero when it is detected that the brake pedal input information is valid.

[0015] In one or more embodiments of the application, in the simulated engine speed calculation module, the transient speed model in the gear shifting state comprises an S-curve function module for the upshift process and an overshoot model or a speed matching model module for the downshift process.

[0016] Compared with the prior art, the virtual fuel vehicle powertrain mapping method in the application has the following beneficial effects: The application can output highly approximate dynamics behavior of a real fuel vehicle, especially in key working conditions such as starting, rapid acceleration and gear shifting, and the fidelity is extremely high.

[0017] The application adopts a high-efficiency algorithm model and rule inference instead of solving complex physical equations, and has low calculation overhead and fast response speed, fully meeting real-time requirements such as hardware-in-the-loop (HIL) and software-in-the-loop (SIL).

[0018] The application establishes an independent slip model function, so that the simulated engine speed can be reasonably higher than the corresponding speed of the wheels in the low-speed high-throttle working condition, and accurately reproduces the “roaring” feeling and power engagement characteristics of the fuel vehicle at the start, which cannot be achieved by the existing simple mapping method.

[0019] The application adopts a modular structure, and the parameters of each functional module are configurable, facilitating engineers to quickly parameterize and adjust different target fuel vehicle models, and improving the flexibility and maintainability of the system.

[0020] The application can output real-time and high-fidelity engine speed and gear information, which can be directly used to drive a sound effect generation system and a force feedback device, and create a more immersive virtual driving experience. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0022] Figure 1 a flow chart of the virtual fuel vehicle powertrain mapping method in an embodiment of the application; Figure 2 a structural schematic diagram of the virtual powertrain mapping framework in an embodiment of the application; Figure 3This is a schematic diagram of a virtual powertrain mapping framework system according to an embodiment of the present invention; Figure 4 This is a throttle mapping curve diagram under different driving styles in one embodiment of the present invention; Figure 5 This is a graph showing the change of vehicle speed threshold with throttle opening in one embodiment of the present invention; Figure 6 This is a complete shift curve diagram according to one embodiment of the present invention; Figure 7 This is a dynamic speed mapping curve during upshifting / downshifting in one embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0024] As described in the background section, existing technologies have significant shortcomings in terms of mapping accuracy, realism, and real-time performance. In particular, when it is necessary to efficiently, in real-time, and accurately convert the operation of electric vehicles into the complex powertrain response of gasoline vehicles, existing methods cannot effectively capture the inherent nonlinear characteristics and dynamic behavior of gasoline vehicles, resulting in limited realism of simulation results, which in turn prolongs the development cycle and increases costs.

[0025] In response to the above technical problems, such as Figure 1 As shown, this invention introduces a virtual fuel vehicle powertrain mapping method, including the following steps: Step 1: Receive real-time input signals from the electric vehicle, including at least vehicle speed information, accelerator pedal input information, and brake pedal input information; Step 2: Based on the accelerator pedal input information, generate a virtual throttle opening through a preset nonlinear mapping relationship; Step 3: Based on the vehicle speed information and the virtual throttle opening, and in conjunction with the preset shifting strategy, determine the target gear of the virtual powertrain; Step 4: Calculate the simulated engine speed. The calculation steps include: in the non-shifting state, calculating the theoretical engine speed based on the vehicle speed information, current gear, and transmission ratio, and introducing a dynamic slip factor related to the vehicle speed information, virtual throttle opening, and brake pedal input information to adjust the theoretical engine speed to simulate the slip effect of the torque converter or clutch; in the shifting state, calculating and generating a smooth speed change curve during the shifting process based on the current speed before shifting and the target speed after shifting, using a preset transient speed model.

[0026] like Figure 2 As shown, this invention provides a dynamic mapping method for virtual fuel powertrains based on input signals from electric vehicles, aiming to solve the problems of insufficient realism and efficiency in existing technologies for powertrain simulation mapping across vehicle types. This invention constructs an intelligent mapping layer that lies between the actual operational input of the electric vehicle and the model of the virtual fuel powertrain. Instead of directly simulating the complex physical details of the fuel powertrain, the mapping layer uses algorithmic models and empirical rules to infer and generate highly realistic internal states and output parameters of the virtual fuel powertrain that match the operation of the electric vehicle. This process is a dynamic transformation from the "representation layer" to the "core power layer," designed to simulate how pedal input and vehicle speed changes affect engine speed, gear position, and clutch state when driving a fuel-powered vehicle. Key mapping features include: Multi-dimensional input to virtual throttle mapping: The pedal input and vehicle speed signals of electric vehicles are mapped into control parameters such as throttle opening of virtual fuel vehicles through nonlinear or adaptive methods.

[0027] Adaptive shift strategy mapping: Based on the mapped virtual throttle opening, vehicle speed and / or other simulated driving parameters, the target gear of the virtual transmission is dynamically determined, and mapping of multiple shift modes is supported.

[0028] Complex powertrain behavior mapping: It can accurately simulate the stable speed of a virtual engine under different gears, speeds and throttle openings, and introduce a mapping model of torque converter / clutch slip effect to ensure reasonable increase of engine speed at low speeds or when starting, reflecting the starting characteristics of real fuel vehicles.

[0029] Dynamic shifting process mapping: Simulates the transient changes in engine speed during virtual gearbox shifting, including nonlinear behaviors such as speed decrease during upshifting and speed increase during downshifting, making the mapping results more realistic.

[0030] This system also adopts a modular design, which makes it easy to configure and expand parameters for different fuel vehicle models, and output simulation data that can be used by subsequent systems.

[0031] likeFigure 3 As shown, the virtual fuel vehicle powertrain mapping system of the present invention includes: an input signal receiving module, a virtual throttle mapping module, a gear status management module, a target gear determination module, and a simulated engine speed calculation module.

[0032] In a further embodiment, the input signal receiving module receives real-time input signals from the electric vehicle, primarily including the vehicle speed signal (V_EV), the accelerator pedal input signal (Pedal_EV, typically 0-100% opening), and the brake pedal input signal (Brake_EV, typically 0-100% opening). These signals are the initial inputs to this mapping method and are acquired at a fixed sampling frequency (e.g., 100Hz or higher). The input signal receiving module then uses the received vehicle speed signal V_EV, accelerator pedal input signal Pedal_EV, and brake pedal input signal Brake_EV as inputs to the subsequent "virtual throttle mapping module," "gear status management module," and "target gear determination module."

[0033] In a further embodiment, the virtual throttle mapping module maps signals by setting an efficient nonlinear mapping function and according to driving style parameters. The nonlinear mapping function can be a Sigmoid, a piecewise polynomial, fuzzy logic, or a neural network. Different driving styles include three driving styles: economy, standard, and sport.

[0034] The Pedal_EV is mapped to a simulated throttle opening (Throttle_Virtual) based on a nonlinear function. Driving style parameters (Economy / Standard / Sport) can be introduced to influence the shape of the mapping curve. Real-time parameter adjustment and online self-learning of mapping relationships are supported, facilitating strategy training and deployment. When the brake pedal is activated, the virtual throttle opening should be forced to zero or an extremely low value to simulate the behavior of releasing the accelerator when braking in a gasoline vehicle. The mapping process must be completed within a real-time frame, and the mapping function can be:

[0035] Where k>0, a larger k value indicates a more sensitive pedal in the initial stage; p>0, the p value controls the overall nonlinearity, p>1 indicates a steeper response, and p<1 indicates a smoother response. Different k and p values ​​can be given according to different driving styles. Pedal_EV and Throttle_Virtual are both normalized to [0, 1], and the output Throttle_Virtual will serve as a key input to the gear state management module, the target gear determination module, and the simulated engine speed calculation module.

[0036] One possible configuration in this embodiment is shown in Table 1, and the corresponding throttle mapping curves under different driving styles are shown in Table 1. Figure 4 As shown.

[0037] Table 1. Parameter Configuration Table

[0038] In a further embodiment, the gear position management module includes a configurable threshold rule set, a finite state machine, and an event-driven mechanism. This module manages the current operating state of the virtual powertrain, including but not limited to driving, upshifting, downshifting, and parking / neutral (P / N) states. This invention does not rely on the explicitly defined state boundaries of traditional finite state machines. Instead, it employs a joint decision-making mechanism based on a soft-threshold hybrid shift map and probabilistic inference, thereby avoiding frequent shifting when throttle fluctuations are large or near critical values, enhancing the physical rationality of shifting behavior. For example, when the electric vehicle speed is below a preset threshold and the virtual throttle opening is zero or below a preset extremely low threshold, the virtual powertrain state can be mapped and switched to the P / N state; when the virtual throttle opening exceeds the threshold, a driving gear (e.g., virtual first gear) is engaged from the P / N state. The gear position management module outputs the current gear position information to the simulated engine speed calculation module and the target gear determination module.

[0039] In a further embodiment, the target gear determination module includes pre-calculated and optimized multi-dimensional shift map data, as well as efficient adaptive shift strategy algorithms (upshift strategy, downshift strategy, forced downshift logic, and braking downshift logic). The shift map is implemented using lookup tables and fast interpolation, avoiding complex online calculations. The shift map can be obtained offline through classification models (such as decision trees, random forests, support vector machines, neural networks, etc.) based on information such as vehicle speed, engine speed, accelerator pedal opening, and gear position under different driving styles of the actual fuel vehicle.

[0040] One embodiment of the gear shift diagram generation step includes collecting data from an actual gasoline vehicle, such as accelerator pedal opening, vehicle speed, current gear, and driving style (0=Eco, 1=Normal, 2=Sport). Example data is shown in the table below: Table 2. Gear shifting data for gasoline vehicles

[0041] Based on the real-time data collected in the table above, a decision tree model is used to generate the following... Figure 5 The image shows the upshift speed threshold curves as a function of throttle opening under different driving styles. Based on these upshift curves, further transformations are performed to generate... Figure 6 The complete shift diagram is shown below.

[0042] In a further embodiment, the target gear determination module dynamically determines a suitable target gear based on the current vehicle speed input of the electric vehicle, the mapped virtual throttle opening, and the current gear of the virtual powertrain.

[0043] Shift graph mapping: Shift logic can be implemented based on a multi-dimensional shift graph, which stores the upshift and downshift speed thresholds for each gear under different virtual throttle openings. These shift graphs can be pre-defined lookup tables or dynamically generated using parameterized functions or interpolation algorithms. Alternatively, they can be trained in a simulation environment using reinforcement learning algorithms to enable the model to learn and optimize the best shift strategy under different driving modes, thereby achieving adaptive or personalized shift mapping. Importantly, the throttle opening thresholds for upshifts and downshifts can be set independently to simulate real transmission lag characteristics. One possible shift strategy mapping is as follows: Upshift strategy: Under high virtual throttle opening (such as simulated full throttle / WOT), the upshift point can be set to trigger at higher vehicle speeds or higher simulated engine speeds (close to the redline) to map maximum acceleration performance. Under low virtual throttle or simulated cruise conditions, the upshift point can be set at lower vehicle speeds to map higher fuel economy.

[0044] Downshifting strategy: This includes mappings for regular downshifting and forced downshifting (Kick-down). Regular downshifting occurs when the electric vehicle's speed is below a certain threshold to keep the simulated engine speed within an effective range. Forced downshifting is triggered when the driver depresses the accelerator pedal deeply and the current virtual gear provides insufficient power, causing the simulated engine speed to rapidly increase to a range that provides greater torque.

[0045] The target gear determination module outputs the target gear (GearTarget) to the simulated engine speed calculation module and drives the gear status management module to enter the upshift or downshift state.

[0046] In a further embodiment, the simulated engine speed calculation module includes a lightweight ideal speed calculation model, an efficient torque converter / clutch slip effect mapping model, speed limiting logic (idle speed IDLE_RPM and redline speed REDLINE_RPM), and a simulated engine speed calculation method in shifting and non-shifting states.

[0047] Working principle: This module calculates simulated engine speed in two states: non-shifting and shifting. In the non-shifting state, a stable simulated engine speed is calculated based on the current gear of the virtual powertrain, the current vehicle speed input, and the mapped virtual throttle opening. First, based on the wheel speed of the electric vehicle, the final drive ratio of the virtual powertrain, and the gear ratio of the current virtual gear, the theoretical engine speed is calculated when the virtual torque converter / clutch is fully locked.

[0048] In real-world gasoline vehicles, considering the effects of torque converter and clutch slippage, especially during start-up and low-speed driving, the torque converter will generate slippage (or the clutch will partially engage), causing the engine speed to be higher than the theoretical speed. This module maps and simulates this phenomenon by introducing a slip factor (S). The slip factor can be dynamically adjusted according to the electric vehicle's speed, the mapped virtual throttle opening, and the preset slip threshold, thereby significantly increasing the simulated engine speed under low-speed, high-throttle conditions, making it more consistent with the "roaring" feeling of starting a real gasoline vehicle. RPM_Simulated=k_Conv*V_EV·GR_Final·GRGear·(1+S(V_EV,Throttle_Virtual,Brake_EV)); Where RPM_Simulated is the simulated engine speed, in units of RPM.

[0049] V_EV is the current speed of the electric vehicle, measured in km / h.

[0050] GRFinal and GRGear represent the final drive ratio and current gear ratio of the virtual fuel powertrain, respectively. kConv is a conversion factor used to convert the product of vehicle speed and drive ratio into the correct unit of engine speed (RPM).

[0051] S(V_EV, ThrottleVirtual, BrakeEV) is a dynamic slip factor function. It represents the slip effect of the virtual fuel vehicle torque converter (or clutch).

[0052] Gear Shift Status: When the gear is shifting up or down, this module simulates the transient changes in engine speed. The speed change curve is achieved through a predefined smoothing function or linear interpolation to ensure a smooth and real-time transition within the set shift time (TShift) to enhance the realism of the simulation.

[0053] Upshift dynamic mapping: During upshifting, the simulated engine speed first drops rapidly (mapping the speed decay when the clutch disengages or torque is interrupted), and then smoothly transitions to the stable speed of the target gear. The upshift speed curve can use a smoothing step function or an S-curve to achieve a more natural and physically consistent speed change curve.

[0054] RPM(t)=RPM_current*(1-s_up(t))+RPM_target*s_up(t); Wherein, s_up(t) is the upshift S-curve related to the shift time; Downshift dynamic mapping: During downshifting, the simulated engine speed first increases rapidly (by rev-matching or speed matching) and then smoothly transitions to the new stable engine speed of the target gear. The downshift speed curve can use either an overshoot model (the engine speed first rises above the target speed (simulated rev-matching) and then falls back to the target speed) or a speed matching model (the engine speed increases directly from the starting speed to the target speed, taking acceleration rate into account).

[0055] Overboost model: RPM_peak = RPM_target * (1 + overboost_ratio); The speed matching model includes: RPM(t) = RPM_current + (RPM_target - RPM_current) * s_down(t); where s_down(t) is the downshift S-curve related to the shift time. The duration of the shift process can be a fixed value or dynamically determined based on the mapped throttle opening, gear difference, vehicle speed, etc.

[0056] Shift time: T_shift=T_base+alpha*abs(Gear_target-Gear_current)+beta*Throttle_Virtual; Where alpha represents the gear span coefficient; beta represents the throttle dynamic coefficient, the larger the throttle, the faster the system response.

[0057] In one embodiment of the present invention, the current speed is 3000 rpm, the target speed for upshifting is 2000 rpm, and the target speed for downshifting is 4000 rpm. The simulated speed curve for the shifting process is as follows: Figure 7 As shown. In addition, it includes speed limiting logic: the calculated simulated engine speed must be limited between the preset idle speed (IDLE_RPM) and redline speed (REDLINE_RPM) to reflect the engine's physical limitations. When the theoretical speed is lower than idle, the speed is clamped at idle to simulate the engine's lowest stable operating point. This module outputs the final simulated engine speed (RPMSimulated) as its core output.

[0058] Compared with existing technologies, the present invention has several significant advantages, as follows: In terms of virtual throttle mapping, this invention offers greater precision while also considering braking coordination. Unlike existing technologies that employ simple linear or two-dimensional lookup table mapping methods, this invention utilizes nonlinear, multi-dimensional (which can be combined with vehicle speed), and even intelligent (such as fuzzy logic and neural networks) mapping methods to better match the virtual throttle opening with the response of an actual gasoline vehicle at different pedal depths. Especially in braking scenarios, it can precisely set the throttle to zero, simulating the behavior of a gasoline vehicle disconnecting power output. Furthermore, all mapping processes are designed with low computational overhead, ensuring real-time performance. For shift strategy mapping, this invention achieves an adaptive and dynamic effect. Existing technologies mostly use fixed threshold shift points, while this invention introduces a multi-dimensional shift map (considering the influence of throttle opening) and adaptive shift strategies (such as kick-down forced downshifting, upshifting and downshifting points in different driving modes), making the shifting behavior of the virtual transmission closer to real-world conditions. This invention accurately simulates the slip effect of the torque converter / clutch, incorporating braking considerations and supporting real-time calculations. Existing methods often ignore or simplify the slip (or clutch partial engagement) phenomenon of the torque converter during start-up and low speeds, resulting in unrealistic simulated engine speeds. Therefore, this invention introduces a slip factor to map and simulate the slip behavior of the torque converter. Unlike existing methods that directly calculate the difference between engine speed and wheel speed to generate virtual slip, this invention establishes an independent slip model function library based on virtual throttle opening, brake pedal opening, and vehicle speed. This allows the simulated engine speed to be reasonably higher than the corresponding wheel speed under low-speed, high-throttle conditions, significantly improving the realism of the simulation. During braking, the model can also simulate changes in engine speed in real-time and reasonably, such as maintaining idle speed or increasing the speed during downshifting, and all calculations are optimized to ensure real-time updates. In simulating transient engine speed during gear shifting, this invention achieves a complete representation. Existing technologies often only update the gear and engine speed after the shift is completed, while this invention can simulate the dynamic transient changes in engine speed during gear shifting (such as the speed drop curve during upshifting and the rapid speed increase during downshifting). This is crucial for improving the realism of the simulation and subsequent sensory reproduction, while also ensuring the smoothness and real-time nature of speed changes. Furthermore, this invention employs a modular structure, offering excellent scalability. Each functional module is independent and its parameters are configurable, with real-time computational performance being a core consideration in the design of all modules. This allows engineers to easily and quickly parameterize and adjust the system for different target fuel-powered vehicle models without having to rebuild complex physical models, greatly improving the system's flexibility and maintainability while ensuring the real-time operation capability of the entire system. The method for dynamically mapping electric vehicle inputs to a virtual fuel powertrain provided by this invention has significant advantages over existing technologies: Significantly improves simulation realism and dynamic response capability: By introducing nonlinear virtual throttle mapping, adaptive shifting strategy, dynamic torque converter / clutch slip effect mapping, and simulation of transient speed changes during shifting, and by specifically considering the influence of brake pedal input on powertrain behavior (such as downshifting and engine braking), this invention can output internal dynamic behavior that is closer to that of a real gasoline vehicle under various operating conditions (including starting, acceleration, cruising, deceleration, and shifting under different throttle openings and braking intensities), greatly improving the fidelity of vehicle simulation. Achieving true real-time simulation: This invention fully considers computational efficiency and response speed during design. The entire mapping process can be completed within an extremely short sampling period, fully meeting the stringent real-time requirements of Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL) simulation testing. This allows virtual testing to interact synchronously with actual hardware or software, greatly accelerating the development and verification process. Supports the development of high-precision virtual test and control systems: Due to the high realism and real-time response capability of the mapping results, the simulation data output by this method can be directly used to drive complex vehicle dynamics models, or as input to real-time hardware-in-the-loop (HIL) / software-in-the-loop (SIL) simulation test platforms, effectively verifying the logic and performance of intelligent driving systems, vehicle control units (VCUs) or other controllers, thereby accelerating the development cycle and reducing physical testing costs. High computational efficiency and low resource consumption: This invention focuses on generating mapping data through efficient algorithmic models and rule inferences, rather than directly solving complex physical equations. This makes its computational complexity far lower than detailed physical model simulations, effectively utilizing computing resources and making it suitable for automotive embedded systems or virtual reality applications requiring rapid response. Modular design, easy to configure and expand: This method adopts a modular structure, with each mapping function being independent and its parameters configurable. This allows engineers to easily and quickly parameterize and adjust the system according to different target fuel vehicle models without having to rebuild complex physical models, greatly improving the system's flexibility and maintainability. This method provides a precise and real-time data source for subsequent sensory reconstruction: the simulated engine speed and gear information output by this method serve as an accurate input data source for generating sound effects and reconstructing driving sensations (such as force feedback pedals and vibration feedback) in a gasoline-powered vehicle, and ensures that this data is updated in real time, which helps to create a more immersive virtual driving experience.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0064] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for mapping a virtual fuel vehicle powertrain, characterized in that, The method includes: Step 1: Receive real-time input signals from the electric vehicle, wherein the input signals include at least vehicle speed information, accelerator pedal input information, and brake pedal input information; Step 2: Based on the accelerator pedal input information, generate a virtual throttle opening through a preset nonlinear mapping relationship; Step 3: Based on the vehicle speed information and the virtual throttle opening, and in conjunction with the preset shifting strategy, determine the target gear of the virtual powertrain; Step 4: Calculate the simulated engine speed. The calculation steps include: in the non-shifting state, calculating the theoretical engine speed based on the vehicle speed information, current gear, and transmission ratio, and introducing a dynamic slip factor related to the vehicle speed information, virtual throttle opening, and brake pedal input information to adjust the theoretical engine speed to simulate the slip effect of the torque converter or clutch; in the shifting state, calculating and generating a smooth speed change curve during the shifting process based on the current speed before shifting and the target speed after shifting, using a preset transient speed model.

2. The virtual fuel vehicle powertrain mapping method according to claim 1, characterized in that, In step 2, the nonlinear mapping relationship can be adjusted based on configurable driving style parameters, which include at least economy mode, standard mode and sport mode, with different modes corresponding to different nonlinear mapping curves.

3. A virtual fuel vehicle powertrain mapping method according to claim 1 or 2, characterized in that, Step 2 further includes: when the brake pedal input information is detected to be valid, the virtual throttle opening is forcibly set to a preset low value or zero.

4. The virtual fuel vehicle powertrain mapping method according to claim 1, characterized in that, In step 3, the shifting strategy is implemented based on a multi-dimensional shifting graph, which defines the upshifting speed threshold and downshifting speed threshold between gears under different virtual throttle openings. The shifting strategy also includes forced downshifting logic, which is triggered when a sharp increase in virtual throttle opening is detected in a short period of time.

5. The virtual fuel vehicle powertrain mapping method according to claim 4, characterized in that, The multi-dimensional shift diagram is generated by training a machine learning model on actual fuel vehicle data. The machine learning model includes decision tree, random forest, support vector machine or neural network.

6. The virtual fuel vehicle powertrain mapping method according to claim 1, characterized in that, The transient speed model in step 4 during gear shifting includes: During upshifting, an S-curve or a smooth step function is used to smoothly reduce the simulated engine speed from the current speed to the target speed. During downshifting, an overshoot model or a speed matching model is used to rapidly increase the simulated engine speed from the current speed to the target speed.

7. The virtual fuel vehicle powertrain mapping method according to claim 1, characterized in that, The method also includes gear state management, which includes managing the driving, upshifting, downshifting and parking / neutral states of the virtual powertrain based on the vehicle speed information and virtual throttle opening, and avoiding frequent gear shifting under critical conditions through soft threshold or probability inference mechanisms.

8. A virtual gasoline vehicle powertrain mapping system, wherein the virtual gasoline vehicle powertrain mapping system is used in the virtual gasoline vehicle powertrain mapping method according to any one of claims 1 to 7, characterized in that, The virtual fuel vehicle powertrain mapping system includes: an input signal receiving module for receiving real-time input signals from electric vehicles, wherein the input signals include at least vehicle speed information, accelerator pedal input information, and brake pedal input information; A virtual throttle mapping module, connected to the input signal receiving module, is used to generate a virtual throttle opening based on the accelerator pedal input information through a preset nonlinear mapping relationship. The target gear determination module is connected to the input signal receiving module and the virtual throttle mapping module, and is used to determine the target gear of the virtual powertrain based on the vehicle speed information and the virtual throttle opening, combined with a preset shifting strategy. The simulated engine speed calculation module is connected to the input signal receiving module, the virtual throttle mapping module, and the target gear determination module, and its configuration is as follows: In non-shifting mode, the theoretical engine speed is calculated based on the vehicle speed information, current gear and transmission ratio, and a dynamic slip factor related to the vehicle speed information, virtual throttle opening and brake pedal input information is introduced to adjust the theoretical engine speed to simulate the slip effect of torque converter or clutch. In shift mode, based on the current speed before shift and the target speed after shift, a smooth speed change curve during the shift process is calculated and generated using a preset transient speed model.

9. The virtual fuel vehicle powertrain mapping system according to claim 8, characterized in that, The virtual throttle mapping module is further configured to adjust the nonlinear mapping relationship according to configurable driving style parameters, and to forcibly set the virtual throttle opening to a preset low value or zero when the brake pedal input information is detected to be valid.

10. The virtual fuel vehicle powertrain mapping system according to claim 8, characterized in that, In the simulated engine speed calculation module, the transient speed model under the shifting state includes an S-curve function module for upshifting and an overshoot model or speed matching model module for downshifting.

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