Energy feedback detection methods and systems for new energy vehicles, new energy vehicles

Abstract

This invention discloses a method and system for detecting energy feedback in new energy vehicles, and the new energy vehicle itself. The method includes the following steps: preprocessing dynamic data of the new energy vehicle to construct a driving mode profile; constructing a simulated driving environment based on the driving mode profile and external environment data using virtual reality technology; training a machine learning model using the preprocessed dynamic data as training samples to learn and identify energy feedback efficiency patterns of the new energy vehicle in various simulated driving environments, and generating an energy feedback strategy after training; evaluating the effectiveness of the energy feedback strategy through the simulated driving environment, adjusting model parameters according to the evaluation results, iteratively optimizing the energy feedback strategy, and applying the energy feedback strategy that meets the preset requirements to the energy feedback detection of new energy vehicles. This invention improves the practical application efficiency and adaptability of the energy feedback system.

Description

Technical Field

[0001] This invention relates to the field of resource recycling technology for new energy vehicles, and particularly to energy feedback detection methods and systems for new energy vehicles, as well as new energy vehicles themselves. Background Technology

[0002] In the rapid development of new energy vehicles, the efficiency of energy recovery systems has become a key factor in improving the overall performance of these vehicles. An energy recovery system refers to the conversion of kinetic energy into electrical energy during braking or deceleration, which is then stored in the battery for future use. Traditional energy recovery testing methods often rely on laboratory environments and standardized testing procedures, which to some extent limits their accuracy and adaptability under real-world driving conditions.

[0003] Existing solutions (such as Chinese invention patent with publication number CN106153354B) are mostly conducted in limited laboratory environments, lacking simulations of diverse road types, traffic conditions, and weather conditions, and thus failing to fully reflect the actual driving environment. Traditional methods are relatively simple in data processing and application, lacking in-depth analysis and utilization of complex driving data, resulting in the failure to fully explore the optimization potential of energy feedback efficiency. Due to the lack of continuous evaluation and optimization mechanisms, the energy feedback strategies under existing solutions are often static and difficult to adapt to constantly changing driving conditions and environments.

[0004] Due to the above factors, the application effect and practicality of existing technical solutions under actual road conditions are limited, making it difficult to meet the growing performance requirements of new energy vehicles. Summary of the Invention

[0005] To address the numerous problems mentioned above, this invention provides an energy feedback detection method and system for new energy vehicles, as well as the new energy vehicle itself. By introducing advanced virtual reality technology, machine learning models, and continuous optimization mechanisms, this invention aims to solve existing technical problems and improve the practical application efficiency and adaptability of energy feedback systems.

[0006] The energy feedback detection method for new energy vehicles includes the following steps:

[0007] Based on the dynamic data of new energy vehicles, data preprocessing is performed and driving mode profiles are constructed.

[0008] A simulated driving environment is constructed using virtual reality technology, based on driving mode profiles and external environment data.

[0009] The preprocessed dynamic data is used as training samples to train a machine learning model, which learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments. After training, an energy feedback strategy is generated.

[0010] The effectiveness of the energy feedback strategy is evaluated through the simulated driving environment. The model parameters are adjusted based on the evaluation results, and the energy feedback strategy is iteratively optimized. The energy feedback strategy that meets the preset requirements is then used for energy feedback detection of new energy vehicles.

[0011] Preferably, the dynamic data includes the speed, acceleration, battery charge, and braking torque of the new energy vehicle during operation.

[0012] Preferably, the data preprocessing and driving mode profile construction includes:

[0013] Remove outliers and irrelevant data;

[0014] Standardize the data;

[0015] Integrate data from different sensors;

[0016] Extract key features from the integrated data;

[0017] By leveraging key features, we can construct profiles representing different driving modes.

[0018] The key features include driving habits and energy efficiency.

[0019] Preferably, the external environment data includes: road type, traffic conditions, and weather conditions.

[0020] Preferably, the simulated driving environment is used to simulate the actual driving scenarios of new energy vehicles;

[0021] The driving scenarios include simulations of different road types, traffic conditions, weather conditions, time periods, and special driving behaviors.

[0022] Preferably, the machine learning model learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments by analyzing and understanding the behavior of the energy feedback system of new energy vehicles under different driving conditions and the impact of driving conditions on energy feedback efficiency.

[0023] The driving conditions include: speed changes and braking torque.

[0024] Preferably, the energy feedback strategy includes: optimized acceleration and braking modes, energy management strategies, driving behavior adjustment suggestions, and optimization guidance for the power system of new energy vehicles.

[0025] Preferably, the evaluation results include: the efficiency and adaptability of the energy feedback strategy, and the stability of the energy feedback system of new energy vehicles;

[0026] The energy feedback system of the new energy vehicle includes: a new energy vehicle power system, a battery management system, and related software control strategies.

[0027] The energy feedback detection system for new energy vehicles includes:

[0028] A profile building module, which preprocesses data and builds a driving mode profile based on dynamic data of new energy vehicles;

[0029] The simulation module constructs a simulated driving environment based on driving mode profiles and external environment data using virtual reality technology.

[0030] The model training module uses the preprocessed dynamic data as training samples to train the machine learning model, learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments, and generates an energy feedback strategy after training is completed.

[0031] The evaluation and application module evaluates the effectiveness of the energy feedback strategy in the simulated driving environment, adjusts the model parameters based on the evaluation results, iteratively optimizes the energy feedback strategy, and applies the energy feedback strategy that meets the preset requirements to the energy feedback detection of new energy vehicles.

[0032] A new energy vehicle, the new energy vehicle including the energy feedback detection system of the new energy vehicle.

[0033] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows:

[0034] (1) This invention uses virtual reality technology to simulate various road types, traffic conditions and weather conditions, and to evaluate energy feedback strategies more comprehensively and realistically.

[0035] (2) This invention utilizes a machine learning model to accurately identify and optimize energy feedback efficiency patterns based on a large amount of collected dynamic data, thereby improving energy utilization efficiency;

[0036] (3) Strategy evaluation and iterative optimization ensure that the strategy remains efficient in various driving environments. This invention also adapts to new driving situations and data.

[0037] (4) The energy feedback strategy of this invention, which has been tested and optimized many times, can be directly applied to new energy vehicles in the real world to improve their energy efficiency and performance. Attached Figure Description

[0038] Figure 1 This is a simplified flowchart illustrating the method of the present invention;

[0039] Figure 2This is a schematic diagram of the execution flow of the method of the present invention;

[0040] Figure 3 This is a schematic block diagram of the system structure of the present invention. Detailed Implementation

[0041] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0042] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0043] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0044] When using expressions such as "at least one of A, B, and C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). Similarly, when using expressions such as "at least one of A, B, or C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, or C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0045] The accompanying drawings illustrate several block diagrams and / or flowcharts. It should be understood that some blocks, or combinations thereof, in the block diagrams and / or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that, when executed by the processor, these instructions can create means for implementing the functions / operations described in these block diagrams and / or flowcharts. The technology of this disclosure can be implemented in hardware and / or software (including firmware, microcode, etc.). Alternatively, the technology of this disclosure can take the form of a computer program product stored on a computer-readable storage medium, which is available for use by or in conjunction with an instruction execution system.

[0046] like Figure 1 - Figure 2 As shown, the energy feedback detection method for new energy vehicles includes the following steps:

[0047] Based on the dynamic data of new energy vehicles, data preprocessing is performed and driving mode profiles are constructed.

[0048] This invention is based on core concepts from data science and machine learning. By collecting dynamic data from new energy vehicles, such as speed, acceleration, battery charge, and braking torque, it is possible to obtain the vehicle's behavioral patterns under different conditions. Data preprocessing is necessary because it ensures data quality and consistency, laying the foundation for subsequent analysis. Constructing a "driving pattern profile" transforms this data into meaningful information, involving the extraction of key features from the data, such as driving habits and energy usage efficiency. The effect of this process is to form a deep understanding of driving behavior, which is crucial for optimizing energy feedback strategies.

[0049] For example, in this invention, if data shows that the battery feedback efficiency is highest at a specific speed, this will directly affect the formulation of the energy feedback strategy.

[0050] Preferably, the dynamic data includes the speed, acceleration, battery charge, and braking torque of the new energy vehicle during operation.

[0051] The dynamic data in this invention is crucial for understanding and optimizing vehicle energy recovery efficiency:

[0052] Speed ​​and acceleration data provide direct information about the vehicle's motion, which is crucial for assessing the efficiency of energy use and recovery; battery charge reflects the state of the energy storage system and is a key indicator for assessing energy feedback potential; braking torque data is directly related to the efficiency of energy recovery during braking.

[0053] Specifically, speed and acceleration reflect the dynamic behavior of a vehicle. In an energy recovery system, changes in vehicle speed directly affect the conversion of kinetic energy, thus affecting the energy fed back to the battery. For example, kinetic energy during braking can be converted into electrical energy for storage.

[0054] Battery charge level data indicates the current status of the energy storage system. The key is to optimize charge / discharge cycles and manage the timing of energy regeneration. For example, when the battery charge is low, energy regeneration should be prioritized to extend driving range.

[0055] Braking torque data is crucial for understanding the efficiency of energy recovery during braking. The greater the braking torque, the more energy can theoretically be recovered. This data helps to precisely tune the braking system to maximize energy recovery.

[0056] The key role of these data types in energy feedback systems is to provide real-time feedback in order to optimize energy management and improve overall energy efficiency.

[0057] Combining this data can help understand the energy recovery potential under specific driving conditions, thereby optimizing energy management strategies and improving energy efficiency.

[0058] If the analysis reveals that energy recovery efficiency is highest under a specific acceleration or braking mode, driving recommendations or vehicle control strategies can be adjusted accordingly.

[0059] Preferably, the data preprocessing and driving mode profile construction includes:

[0060] Remove outliers and irrelevant data; ensure data quality and avoid erroneous data affecting the accuracy of the model.

[0061] Standardize the data; unify the format and scale of different data sources to make model processing more efficient.

[0062] Integrate data from different sensors; combine data from different sensors to provide a comprehensive view of driving behavior.

[0063] Extract key features from the integrated data; identify the main factors affecting energy feedback efficiency, such as driving habits and energy usage efficiency.

[0064] By leveraging key features, we can construct profiles representing different driving modes; based on these key features, we can form a comprehensive understanding of different driving modes, which helps to optimize energy recovery strategies.

[0065] The key features include driving habits and energy efficiency.

[0066] The effect of this data preprocessing and driving mode profiling process is to improve the accuracy and efficiency of energy feedback detection, thereby optimizing energy management through a better understanding of driving behavior and vehicle performance. For example, by analyzing driving mode profiling, it is possible to identify situations where battery feedback efficiency is highest under specific driving habits, thus guiding adjustments to driving behavior or optimization of vehicle settings.

[0067] A simulated driving environment is constructed using virtual reality technology, based on driving mode profiles and external environment data.

[0068] The principle behind this invention, which uses virtual reality technology to construct a simulated driving environment, is to create a near-realistic driving experience to better evaluate and test energy recovery strategies. This environment combines driving mode profiles (reflecting driving habits and energy usage efficiency) with external environmental data (such as road type, traffic conditions, and weather conditions) to simulate the performance of new energy vehicles under various real-world driving conditions.

[0069] The application of virtual reality technology in simulated driving environments mainly involves the following aspects:

[0070] Environmental simulation: VR technology can accurately simulate various road conditions and traffic scenarios, such as city streets, highways, and mountain road conditions.

[0071] Weather and time simulation: It can simulate different weather conditions (sunny, rainy, snowy) and different times of day (daytime, nighttime) driving environments.

[0072] Interactivity: VR offers a high degree of interactivity, allowing simulation of the impact of various driving operations on the vehicle's energy system.

[0073] Data integration: Integrate collected driving mode data and external environment data into the simulation environment to create a highly realistic driving experience.

[0074] This technology is highly effective in simulating complex driving scenarios, especially in testing and optimizing energy recovery systems, providing a risk-free and cost-effective solution.

[0075] This invention enables the testing and optimization of energy recovery strategies in a controlled and repeatable environment without actual road testing. For example, driving conditions in rain or snow can be simulated to evaluate the performance of the energy recovery system under these extreme conditions.

[0076] Preferably, the external environment data includes: road type, traffic conditions, and weather conditions.

[0077] In this invention, the principle of considering external environmental data (road type, traffic conditions, and weather conditions) is to more comprehensively evaluate the performance of the energy recovery system in real-world driving environments. Different road types (such as urban roads and highways) may lead to differences in driving behavior, affecting the efficiency of energy recovery. Traffic conditions (such as congestion and smooth traffic) also affect the frequency of braking and acceleration, thus affecting energy recovery. Weather conditions (such as rain and snow) may affect braking performance and tire-road friction, thus affecting energy recovery efficiency. Comprehensive consideration of these data helps to more accurately simulate and test the performance of energy recovery strategies under various real-world driving conditions.

[0078] Preferably, the simulated driving environment is used to simulate the actual driving scenarios of new energy vehicles;

[0079] The driving scenarios include simulations of different road types, traffic conditions, weather conditions, time periods, and special driving behaviors.

[0080] In this invention, a simulated driving environment is used to simulate the actual driving scenarios of new energy vehicles. The principle behind this is to provide a comprehensive and realistic testing environment. This environment includes different road types (such as urban roads and highways), traffic conditions (such as traffic congestion or free flow), weather conditions (such as sunny, rainy, and snowy days), and different time periods. Furthermore, special driving behaviors, such as rapid acceleration and sudden braking, can be simulated. Such simulations help evaluate the energy recovery efficiency of new energy vehicles under various conditions, thereby optimizing energy recovery strategies. For example, the performance of the energy recovery system in rainy, congested traffic can be tested, and strategies can be adjusted to improve efficiency. This method enables comprehensive evaluation and optimization of the energy recovery system under different scenarios.

[0081] The preprocessed dynamic data is used as training samples to train a machine learning model, which learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments. After training, an energy feedback strategy is generated.

[0082] The core principle of this stage is to utilize machine learning models to analyze and learn the energy recovery efficiency of new energy vehicles under different driving conditions. Using preprocessed dynamic data (such as speed, acceleration, and battery charge) as training samples, the model can identify energy recovery patterns in various simulated driving environments (including different road types, traffic conditions, and weather conditions). After training, the model can generate targeted energy recovery strategies aimed at optimizing the vehicle's energy use and recovery efficiency. For example, the model may discover that under certain traffic or weather conditions, specific driving modes maximize energy recovery. This strategy can guide the driver or vehicle control system to improve energy efficiency.

[0083] Regarding how to use energy feedback strategies generated by machine learning models to guide drivers or vehicle control systems, the following aspects are included:

[0084] How the generated energy feedback strategy is integrated into the vehicle control system, and how driving behavior is adjusted in real time to improve energy efficiency;

[0085] Analyze how to convey energy recovery strategy recommendations to the driver through the vehicle interface, such as through instrument panel displays or audio prompts;

[0086] Evaluate the long-term impact of these strategies on energy consumption, battery life, and overall driving experience.

[0087] To explore how vehicle systems automatically adjust strategies to maintain optimal energy return efficiency based on driver behavior and changes in the external environment.

[0088] This in-depth analysis helps to understand how machine learning models function in real-world driving and how technology can improve the energy efficiency of new energy vehicles.

[0089] Preferably, the machine learning model learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments by analyzing and understanding the behavior of the energy feedback system of new energy vehicles under different driving conditions and the impact of driving conditions on energy feedback efficiency.

[0090] The driving conditions include: speed changes and braking torque.

[0091] The above describes how machine learning models analyze and understand the energy recovery system behavior of new energy vehicles under different driving conditions. This includes analyzing how factors such as speed variations and braking torque affect energy recovery efficiency. By learning these behavioral patterns, the model can identify the optimal energy recovery efficiency pattern in various simulated driving environments.

[0092] For example, the model might discover that energy recovery efficiency is highest within a specific speed range or braking torque level. Such findings can guide drivers to adjust their driving habits or be used to refine the vehicle's energy management system to optimize efficiency.

[0093] Preferably, the energy feedback strategy includes: optimized acceleration and braking modes, energy management strategies, driving behavior adjustment suggestions, and optimization guidance for the power system of new energy vehicles.

[0094] Optimize acceleration and braking modes: Analyze data to determine the acceleration and braking modes with the highest energy recovery efficiency. For example, braking at certain speeds may recover energy more efficiently.

[0095] Energy management strategy: Based on vehicle usage patterns and driving environment, optimize the battery charging and discharging process to balance energy demand and supply.

[0096] Driving behavior adjustment suggestions: Based on model analysis, suggestions are provided to improve driving habits in order to enhance energy efficiency.

[0097] Powertrain optimization guidance: Provides suggestions on the configuration and adjustment of the vehicle's powertrain system to improve overall energy management.

[0098] These strategies comprehensively optimize energy recovery efficiency from different aspects, ensuring maximum energy use in actual driving.

[0099] The effectiveness of the energy feedback strategy is evaluated through the simulated driving environment. The model parameters are adjusted based on the evaluation results, and the energy feedback strategy is iteratively optimized. The energy feedback strategy that meets the preset requirements is then used for energy feedback detection of new energy vehicles.

[0100] The principle behind evaluating energy feedback strategies through simulated driving environments is to test the effectiveness of these strategies under real-world driving conditions. This includes evaluating the strategy's performance under different road, traffic, and weather conditions. Based on these evaluation results, the parameters of the machine learning model can be adjusted to further optimize the energy feedback strategy. For example, if it is found that energy feedback efficiency is low under specific traffic conditions, the model can be adjusted to adapt to this situation. Through such an iterative optimization process, the final determined energy feedback strategy will be more accurate and efficient, suitable for actual energy feedback detection in new energy vehicles.

[0101] The evaluation and optimization process includes:

[0102] First, existing energy recovery strategies are tested in simulated environments. This includes evaluating the efficiency of the strategies under various simulated driving conditions, such as different weather conditions, road types, and traffic conditions. The evaluation results reveal the strengths and weaknesses of the strategies, such as their energy recovery efficiency under specific conditions.

[0103] Next, based on these results, the parameters of the machine learning model are adjusted, such as changing the weights of specific features, to better adapt to different driving situations. Through multiple iterations, these strategies are gradually optimized until they reach the preset performance standards.

[0104] Ultimately, these optimized strategies can be applied to real-world energy feedback detection to improve the energy efficiency and performance of new energy vehicles. This process ensures that the strategies operate effectively in diverse real-world driving environments.

[0105] Preferably, the evaluation results include: the efficiency and adaptability of the energy feedback strategy, and the stability of the energy feedback system of new energy vehicles;

[0106] The energy feedback system of the new energy vehicle includes: a new energy vehicle power system, a battery management system, and related software control strategies.

[0107] The evaluation focuses on the efficiency and adaptability of energy recovery strategies, as well as the stability of energy recovery systems in new energy vehicles. This covers the powertrain, battery management system, and software control strategies of new energy vehicles. In principle, these evaluation metrics reflect the performance of energy recovery strategies in practical applications, including their applicability and long-term stability under different driving conditions. For example, an evaluation of strategy efficiency might consider the amount of energy recovered during high-speed driving, while a stability evaluation might focus on the performance retention of the battery and powertrain under frequent urban driving conditions.

[0108] In practical applications, these evaluation criteria are as follows:

[0109] Efficiency of the energy recovery strategy: The energy recovery rate of the energy recovery strategy is tested by simulating different conditions (such as high-speed driving and urban roads). For example, the efficiency of the strategy in urban environments with frequent stopping and starting is evaluated.

[0110] Strategy adaptability: Examine the performance of the strategy under varying driving environments (such as different weather conditions and traffic densities). For example, analyze the adaptability of the strategy under rainy conditions.

[0111] System stability: Long-term monitoring of the performance stability of the battery and power system after implementing energy feedback strategies. For example, checking the battery health status after continuous use of a specific strategy.

[0112] These standards ensure that energy feedback strategies are not only theoretically effective, but also stable and reliable in practical applications.

[0113] like Figure 3 As shown, the energy feedback detection system for new energy vehicles includes:

[0114] A profile building module, which preprocesses data and builds a driving mode profile based on dynamic data of new energy vehicles;

[0115] Based on dynamic data from new energy vehicles (such as speed, acceleration, battery status, and braking torque), data preprocessing (cleaning and standardization) is performed to construct driving mode profiles. This involves analyzing driving habits and energy usage efficiency to form a comprehensive understanding of driving behavior. It can provide accurate driving behavior data, laying the foundation for simulation environments and strategy formulation. If the analysis finds that energy feedback efficiency is highest within a specific speed range, this can be used to guide subsequent strategy development.

[0116] The simulation module constructs a simulated driving environment based on driving mode profiles and external environment data using virtual reality technology.

[0117] Using virtual reality technology, a simulated driving environment is constructed based on driving pattern profiles and external environmental data (such as road type, traffic conditions, and weather conditions). This allows energy feedback detection to be performed under various simulated scenarios. It provides a comprehensive and diverse testing platform capable of evaluating energy feedback strategies under different hypothetical driving environments.

[0118] The model training module uses the preprocessed dynamic data as training samples to train the machine learning model, learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments, and generates an energy feedback strategy after training is completed.

[0119] This module uses preprocessed dynamic data to train a machine learning model to identify energy feedback efficiency patterns in various simulated driving environments. After training, the model can generate optimized energy feedback strategies. This improves the accuracy and adaptability of the energy feedback strategy, ensuring its effectiveness in multiple environments.

[0120] The evaluation and application module evaluates the effectiveness of the energy feedback strategy in the simulated driving environment, adjusts the model parameters based on the evaluation results, iteratively optimizes the energy feedback strategy, and applies the energy feedback strategy that meets the preset requirements to the energy feedback detection of new energy vehicles.

[0121] The actual effectiveness of the energy feedback strategy is evaluated through environmental simulation. Model parameters are adjusted based on the evaluation results, and the strategy is iteratively optimized. Finally, the validated strategy is applied to actual energy feedback testing. This ensures the effectiveness and reliability of the energy feedback strategy and improves the energy efficiency of new energy vehicles.

[0122] A new energy vehicle, the new energy vehicle including the energy feedback detection system of the new energy vehicle.

[0123] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0124] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] 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 instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] 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.

[0127] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0128] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0129] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0130] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0131] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting energy feedback in new energy vehicles, characterized in that, Includes the following steps: Based on the dynamic data of new energy vehicles, data preprocessing is performed and driving mode profiles are constructed. The dynamic data includes the speed, acceleration, battery charge, and braking torque of the new energy vehicle during operation. The data preprocessing and driving mode profiling include: Remove outliers and irrelevant data; Standardize the data; Integrate data from different sensors; Extract key features from the integrated data; By leveraging key features, we can construct profiles representing different driving modes. The key features include: driving habits and energy efficiency; A simulated driving environment is constructed using virtual reality technology, based on driving mode profiles and external environment data. The preprocessed dynamic data is used as training samples to train a machine learning model, which learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments. After training, an energy feedback strategy is generated. The machine learning model learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments by analyzing and understanding the behavior of the energy feedback system of new energy vehicles under different driving conditions, as well as the impact of driving conditions on energy feedback efficiency. The driving conditions include speed changes and braking torque. The simulated driving environment is used to simulate the actual driving scenarios of new energy vehicles; The driving scenarios include simulations of different road types, traffic conditions, weather conditions, time periods, and special driving behaviors. The effectiveness of the energy feedback strategy is evaluated through the simulated driving environment. The model parameters are adjusted according to the evaluation results, and the energy feedback strategy is iteratively optimized. The energy feedback strategy that meets the preset requirements is used for energy feedback detection of new energy vehicles. The evaluation results include: the efficiency and adaptability of the energy feedback strategy, and the stability of the energy feedback system for new energy vehicles; The energy feedback system of the new energy vehicle includes: a new energy vehicle power system, a battery management system, and related software control strategies.

2. The energy feedback detection method for new energy vehicles according to claim 1, characterized in that, The external environmental data includes: road type, traffic conditions, and weather conditions.

3. The energy feedback detection method for new energy vehicles according to claim 1, characterized in that, The energy feedback strategy includes: optimized acceleration and braking modes, energy management strategies, driving behavior adjustment suggestions, and optimization guidance for the power system of new energy vehicles.

4. An energy feedback detection system for new energy vehicles, used to implement the energy feedback detection method for new energy vehicles as described in any one of claims 1-3, characterized in that, include: A profile building module, which preprocesses data and builds a driving mode profile based on dynamic data of new energy vehicles; The simulation module constructs a simulated driving environment based on driving mode profiles and external environment data using virtual reality technology. The model training module uses the preprocessed dynamic data as training samples to train the machine learning model, learns to identify the energy feedback efficiency patterns of new energy vehicles in various simulated driving environments, and generates an energy feedback strategy after training is completed. The evaluation and application module evaluates the effectiveness of the energy feedback strategy in the simulated driving environment, adjusts the model parameters based on the evaluation results, iteratively optimizes the energy feedback strategy, and applies the energy feedback strategy that meets the preset requirements to the energy feedback detection of new energy vehicles.

5. A new energy vehicle, characterized in that, The new energy vehicle includes the energy feedback detection system for new energy vehicles as described in claim 4.

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