Range-extending type electric vehicle engine rotating speed limiting method and device and electronic equipment

Abstract

The invention discloses an extended-range electric vehicle engine rotating speed limiting method and device and electronic equipment. The method comprises the steps that a current road surface type is determined based on current positioning information and road surface type distribution information returned by a cloud server; recognizing and obtaining a current pavement material based on the current driving noise, the current driving vibration and the current pavement image; when the current road surface material conforms to the current road surface type, a first rotating speed threshold value is determined according to the current driving speed and the current road surface material, and rotating speed limiting control is conducted on an engine of the target vehicle according to the first rotating speed threshold value; when the current road surface material does not conform to the current road surface type, a second rotating speed threshold value is determined according to the current driving speed and the current road surface material, and rotating speed limiting change control is conducted on the engine of the target vehicle according to the preset change rate till the rotating speed limiting numerical value of the engine of the target vehicle reaches the second rotating speed threshold value. The driving experience of drivers and passengers is improved, and the method can be applied to the technical field of vehicle control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to an engine speed limiting method, device and electronic equipment for an extended-range electric vehicle. BACKGROUND

[0002] An extended-range electric vehicle (REEV) is also known as an extended-range hybrid or an extended-range new energy vehicle. The vehicle can achieve all dynamic performance in pure electric mode, and when the onboard rechargeable battery cannot meet the range requirement, the onboard auxiliary power generation device is turned on to provide power for the power system, thereby extending the range of the vehicle. The vehicle is a series plug-in hybrid electric vehicle.

[0003] The extended-range electric vehicle can start the range extender to maintain vehicle travel and charge the battery when the power battery energy is low. The range extender is usually composed of an engine and a generator. According to the identified driver setting mode, driving demand power, SOC and other conditions, the engine rotation is controlled to maintain vehicle travel and charge the battery. However, when the range extender is working, the continuous operation of the engine will produce noise and vibration, which will reduce the driving comfort under the superposition of wind noise and tire noise, and affect the driving experience of the driver and passenger. SUMMARY

[0004] The present application aims to at least partially solve one of the problems in the prior art.

[0005] To this end, one object of the present application is to provide an engine speed limiting method for an extended-range electric vehicle, which can match the corresponding engine speed threshold according to the driving speed and road surface material, thereby limiting the engine speed of the extended-range electric vehicle, reducing the noise and vibration generated by the engine during driving, and improving the driving experience of the driver and passenger.

[0006] Another object of the present application is to provide an engine speed limiting device for an extended-range electric vehicle.

[0007] In order to achieve the above technical purpose, the technical solution adopted by the present application comprises: On the one hand, the present application provides an engine speed limiting method for an extended-range electric vehicle, comprising the following steps: Obtaining the navigation start and end path of the target vehicle, uploading the navigation start and end path to the cloud server, and returning the road surface type distribution information corresponding to the navigation start and end path from the cloud server; Determining the current positioning information of the target vehicle, and determining the current road surface type according to the current positioning information and the road surface type distribution information; acquire a current driving speed, a current driving noise, a current driving vibration and a current road surface image of the target vehicle, and identify a current road surface material according to the current driving noise, the current driving vibration and the current road surface image; When the current road surface material matches the current road type, a first rotation speed threshold is determined according to the current driving speed and the current road surface material, and a rotation speed limiting control is performed on the engine of the target vehicle according to the first rotation speed threshold; When the current road surface material does not match the current road type, a second rotation speed threshold is determined according to the current driving speed and the current road surface material, and a rotation speed limiting change control is performed on the engine of the target vehicle according to a preset change rate until the rotation speed limiting value of the engine of the target vehicle reaches the second rotation speed threshold.

[0008] Further, in an embodiment of the present application, the navigation start-end path of the target vehicle is acquired, and the navigation start-end path is uploaded to a cloud server, so that the cloud server returns road type distribution information corresponding to the navigation start-end path, which specifically comprises: The navigation start-end path is acquired through a car machine system, and the navigation start-end path is uploaded to the cloud server; The cloud server determines a plurality of navigation sections according to the navigation start-end path, and determines a road type label corresponding to each navigation section according to a preset road network database carrying road type; The path interval information corresponding to the navigation section is determined according to the start point coordinates and the end point coordinates of the navigation section, the navigation start-end path is divided into a plurality of navigation path intervals according to the path interval information, and each navigation path interval is labeled according to the road type label, so as to generate the road type distribution information; The cloud server returns the road type distribution information to the car machine system; The road type label includes asphalt pavement, cement pavement, gravel pavement and soil pavement.

[0009] Further, in an embodiment of the present application, the current road type is determined according to the current positioning information and the road type distribution information, which specifically comprises: The current navigation path interval of the target vehicle is determined according to the current positioning information and the navigation start-end path; The current road type corresponding to the current navigation path interval is determined according to the road type distribution information.

[0010] Further, in an embodiment of the present application, the current road surface material is identified according to the current driving noise, the current driving vibration and the current road surface image, which specifically comprises: extracting a mel-frequency cepstral coefficient of the current driving noise, and determining a driving noise feature according to the mel-frequency cepstral coefficient; performing Fourier transform on the current driving vibration to obtain a driving vibration frequency spectrum, and determining a vibration energy distribution feature according to the driving vibration frequency spectrum; performing image feature extraction on the current road surface image to obtain a road surface texture feature, a road surface color feature and a road surface edge feature; performing feature fusion on the driving noise feature, the vibration energy distribution feature, the road surface texture feature, the road surface color feature and the road surface edge feature to obtain multi-modal feature data; inputting the multi-modal feature data into a pre-trained road surface material identification model to obtain the current road surface material.

[0011] Further, in an embodiment of the present application, the road surface material identification model is obtained by the following steps: obtaining sample driving noise, sample driving vibration and sample road surface image in a historical driving scene; extracting a driving noise feature sample according to the sample driving noise, extracting a vibration energy distribution feature sample according to the sample driving vibration, and extracting a road surface texture feature sample, a road surface color feature sample and a road surface edge feature sample according to the sample road surface image; constructing a training sample according to the driving noise feature sample, the vibration energy distribution feature sample, the road surface texture feature sample, the road surface color feature sample and the road surface edge feature sample, and determining a road surface material label corresponding to the training sample through artificial labeling; inputting the training sample into a pre-constructed multi-modal convolutional neural network to obtain a road surface material identification result; determining a loss value according to the road surface material identification result and the road surface material label; updating parameters of the multi-modal convolutional neural network according to the loss value through a back propagation algorithm to obtain the trained road surface material identification model; wherein the road surface material label is a two-level label under a road surface type.

[0012] Further, in an embodiment of the present application, the first rotation speed threshold is determined according to the current driving speed and the current road surface material, and the engine of the target vehicle is controlled in rotation speed limitation according to the first rotation speed threshold, which specifically comprises: acquire a rotation speed threshold mapping table calibrated in advance through experiments; query the rotation speed threshold mapping table according to the current driving speed and the current road surface material to obtain the first rotation speed threshold; maintain the rotation speed limit value of the engine of the target vehicle as the first rotation speed threshold.

[0013] Further, in an embodiment of the present application, the second rotation speed threshold is determined according to the current driving speed and the current road surface material, and the rotation speed limit value of the engine of the target vehicle is gradually increased / decreased according to a preset change rate until the second rotation speed threshold is reached, which specifically comprises: acquire a rotation speed threshold mapping table calibrated in advance through experiments; query the rotation speed threshold mapping table according to the current driving speed and the current road surface material to obtain the second rotation speed threshold; control the rotation speed limit value of the engine of the target vehicle to gradually increase / decrease according to the change rate until the second rotation speed threshold is reached.

[0014] On the other hand, an embodiment of the present application provides an engine rotation speed limiting device for extended-range electric vehicles, comprising: a cloud interaction module configured to acquire a navigation start-stop path of a target vehicle, upload the navigation start-stop path to a cloud server, and make the cloud server return road surface type distribution information corresponding to the navigation start-stop path; a road surface type determination module configured to determine current positioning information of the target vehicle, and determine a current road surface type according to the current positioning information and the road surface type distribution information; a road surface material identification module configured to acquire a current driving speed, a current driving noise, a current driving vibration, and a current road surface image of the target vehicle, and identify a current road surface material according to the current driving noise, the current driving vibration, and the current road surface image; a first rotation speed limiting module configured to, when the current road surface material is consistent with the current road surface type, determine a first rotation speed threshold according to the current driving speed and the current road surface material, and perform rotation speed limiting control on the engine of the target vehicle according to the first rotation speed threshold; a second rotation speed limiting module configured to, when the current road surface material is not consistent with the current road surface type, determine a second rotation speed threshold according to the current driving speed and the current road surface material, and perform rotation speed limiting change control on the engine of the target vehicle according to a preset change rate until the rotation speed limit value of the engine of the target vehicle reaches the second rotation speed threshold.

[0015] In another aspect, an electronic device is provided, comprising: at least one processor; at least one memory configured to store at least one program; the at least one processor, when executing the at least one program, is caused to implement the above method for limiting engine speed of extended-range electric vehicle.

[0016] In another aspect, a computer-readable storage medium is provided, which stores a computer program executable by a processor, and the computer program, when executed by the processor, implements the above method for limiting engine speed of extended-range electric vehicle.

[0017] In another aspect, a computer program product is provided, which comprises a computer program, and the computer program, when executed by a processor, implements the above method for limiting engine speed of extended-range electric vehicle.

[0018] The advantages and beneficial effects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be learned through the practice of the present application: The embodiment of the present application determines the current road surface type based on the road surface type distribution information returned by the cloud server, determines the current road surface material based on the current driving noise, the current driving vibration and the current road surface image, determines that the vehicle is stably driving on the road with the corresponding road surface type and the corresponding road surface material when the current road surface material meets the current road surface type, determines the first speed threshold value according to the current driving speed and the current road surface material at this time, and performs speed limit control on the vehicle engine according to the first speed threshold value to avoid excessive noise and vibration generated by the vehicle engine with excessive speed. When the current road surface material does not meet the current road surface type, it is determined that the vehicle is driving in the transition zone where the road surface type changes, the second speed threshold value is determined according to the current driving speed and the current road surface material at this time, the speed limit change control is performed on the vehicle engine according to the preset change rate until the speed limit value reaches the second speed threshold value, and the excessive speed change range of the vehicle engine is avoided to affect the vehicle stability. The embodiment of the present application can match the corresponding engine speed threshold value according to the driving speed and the road surface material, thereby limiting the engine speed of the extended-range electric vehicle, reducing the noise and vibration generated by the engine during driving, and improving the driving experience of the driver and the passenger. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following introduces the drawings needed to be used in the embodiments of the present application. It should be understood that the drawings introduced in the following are only for the convenience of describing some of the embodiments of the technical solutions in the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0020] Figure 1 A step flow chart of a range extended electric vehicle engine speed limiting method provided by the embodiment of the present application is shown in the figure. Figure 2 A structure block diagram of a range extended electric vehicle engine speed limiting device provided by the embodiment of the present application is shown in the figure. Figure 3 A structure block diagram of an electronic device provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application is further described in detail below by combining with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. When the following description relates to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the embodiments of the present application, and they are only examples of devices and methods consistent with some aspects of the embodiments of the present application as described in the appended claims.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.

[0023] The range-extender electric vehicle engine speed limiting method provided by the embodiments of the present application can be applied to a terminal, can be applied to a server, and can also be software running in the terminal or the server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, and the like, but is not limited thereto. The server end can be configured as a stand-alone physical server, can be configured as a server cluster composed of multiple physical servers or a distributed system, can be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, and big data and artificial intelligence platform, and the server can also be a node server in a blockchain network. The software can be an application that implements the range-extender electric vehicle engine speed limiting method, and the like, but is not limited to the above forms.

[0024] The present application can be applied in a variety of general purpose or special purpose computer systems environments or configurations. Examples of well known computing systems, environments, and / or configurations that can be suitable for use with the application include personal computers, server computers, handheld or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media including memory storage devices.

[0025] It should be noted that in each of the specific embodiments of the present application, when relevant processing needs to be performed on data related to the identity or characteristics of the user, such as user information, user behavior data, user history data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain sensitive personal information of the user, the separate permission or separate consent of the user will be obtained through a pop-up window or a jump to a confirmation page, and after obtaining the separate permission or separate consent of the user, the necessary user-related data for enabling the embodiments of the present application to normally operate will be obtained.

[0026] Reference Figure 1 The embodiments of the present application provide a range-extender electric vehicle engine speed limiting method, which specifically includes the following steps: S101: Obtain the navigation start and end paths of the target vehicle, upload the navigation start and end paths to a cloud server, and have the cloud server return road type distribution information corresponding to the navigation start and end paths; S102, determining the current positioning information of the target vehicle, and determining the current road type based on the current positioning information and the road type distribution information; S103, obtaining the current driving speed, current driving noise, current driving vibration, and current road surface image of the target vehicle, and identifying the current road surface material based on the current driving noise, current driving vibration, and current road surface image; S104: When the current road surface material meets the current road surface type, determine a first speed threshold based on the current driving speed and the current road surface material, and perform speed limit control on the engine of the target vehicle based on the first speed threshold; S105. When the current road surface material does not conform to the current road surface type, a second speed threshold is determined based on the current driving speed and the current road surface material, and the speed limit change control of the target vehicle's engine is performed according to a preset change rate until the speed limit value of the target vehicle's engine reaches the second speed threshold.

[0027] Specifically, the embodiment of the present invention determines the current road type based on road type distribution information returned by a cloud server, and obtains the current road material based on current driving noise, current driving vibration, and current road image recognition. When the current road material matches the current road type, it is determined that the vehicle is stably traveling on a road of the corresponding road type and material. A first speed threshold is determined based on the current driving speed and the current road material. The vehicle engine speed is limited based on the first speed threshold to prevent excessive noise and vibration from being generated by excessive engine speed. When the current road material does not match the current road type, it is determined that the vehicle is traveling in a transition zone where the road type changes. A second speed threshold is determined based on the current driving speed and the current road material. The vehicle engine speed is limited based on a preset rate of change until the speed limit reaches the second speed threshold, preventing excessive engine speed fluctuations that affect vehicle stability. The embodiment of the present invention can match the corresponding engine speed threshold based on the driving speed and road material, thereby limiting the engine speed of the extended-range electric vehicle, reducing engine noise and vibration during driving and improving the driving experience for the driver and passengers.

[0028] As an optional implementation, obtaining the navigation start and end paths of the target vehicle and uploading the navigation start and end paths to a cloud server so that the cloud server returns road type distribution information corresponding to the navigation start and end paths, which specifically includes: S1011. Obtain the navigation start and end paths through the vehicle computer system and upload the navigation start and end paths to the cloud server; S1012, determine a plurality of navigation segments according to the navigation start and end path through the cloud server, and determine the road surface type label corresponding to each navigation segment according to the preset road network database carrying the road surface type; S1013, determine the path interval information corresponding to the navigation segment according to the start point coordinates and the end point coordinates of the navigation segment, divide the navigation start and end path into a plurality of navigation path intervals according to the path interval information, and label each navigation path interval according to the road surface type label, and generate the road surface type distribution information; S1014, return the road surface type distribution information to the car system through the cloud server; The road surface type label includes asphalt pavement, cement pavement, gravel pavement and soil pavement.

[0029] Specifically, the embodiment of the application uploads the navigation start and end path to the cloud server through the car system, and labels the intervals using the road network database of the cloud server, thereby obtaining the road surface type distribution information corresponding to the navigation address path. The specific process is as follows: 1. The car system collects and uploads the navigation start and end path Path information collection: the car system obtains the start point coordinates (such as the real-time GPS position of the current vehicle) and the end point coordinates (the target position set by the user) of the navigation through user input (such as manual input of the destination, voice instruction) or historical trip planning. The coordinate format is usually latitude and longitude (WGS84 coordinate system), and contains metadata such as timestamp, vehicle ID, etc., to ensure the uniqueness and traceability of the path.

[0030] Data encryption and uploading: the car system performs lightweight encryption (such as HTTPS protocol) on the start and end path information, and uploads the data to the cloud server through cellular network (4G / 5G) or Wi-Fi. The uploaded content includes: start point (Lng1, Lat1), end point (Lng2, Lat2), request time, vehicle model (optional, used to adapt to the navigation needs of different vehicle models).

[0031] 2. The cloud server generates navigation segments and matches road surface types Path planning and segment splitting: after the cloud server receives the start and end path, it calls the built-in path planning algorithm (such as Dijkstra algorithm), combines real-time traffic data (such as congestion situation, road closure information) to generate the optimal navigation path. Then, according to the road intersection, turning point or preset length threshold (such as every 500 meters or every independent road), the complete path is split into a plurality of continuous navigation segments (such as "segment 1: start point A to intersection B", "segment 2: intersection B to highway entrance C", etc.).

[0032] Road network database matching road surface type label: The cloud server accesses a preset road network database carrying road surface types, which contains the accurate coordinates of national / regional roads, road levels (expressway, national highway, county road, etc.), and road surface type labels (such as asphalt pavement, cement pavement, gravel pavement, and soil pavement). Through spatial coordinate matching technology (such as geographic hashing and R-tree indexing), the corresponding road surface type label is matched for each navigation road segment.

[0033] 3. Generating road surface type distribution information Path interval information determination: For each navigation road segment, its start coordinate (S_Lng, S_Lat) and end coordinate (E_Lng, E_Lat) are extracted, which are defined as the path interval of the road segment. For example, the interval of road segment 1 is [(Lng1, Lat1), (Lng3, Lat3)], which represents the path range from the start point to the first intersection.

[0034] Path interval labeling and integration: The cloud server binds the path interval of each navigation road segment with the corresponding road surface type label to generate road surface type distribution information, which contains road segment ID, interval coordinate, and road surface type label, and can directly reflect the road surface conditions of different intervals in the navigation path.

[0035] 4. Cloud server returns data to the vehicle system The cloud server compresses the generated road surface type distribution information and returns it to the vehicle system through the original communication link. After receiving the data, the vehicle system performs parsing and visualization processing: the road surface types of each path interval are labeled on the navigation map with different colors or icons (such as green for asphalt pavement, black for cement pavement, gray for gravel pavement, and yellow for soil pavement), and the user can click on the road segment to view detailed information (such as road surface roughness and recommended speed).

[0036] Further as an optional implementation, the current road surface type is determined according to the current positioning information and the road surface type distribution information, which specifically includes: S1021, determining the current navigation path interval of the target vehicle according to the current positioning information and the navigation start and end path; S1022, determining the current road surface type corresponding to the current navigation path interval according to the road surface type distribution information.

[0037] Specifically, the specific position of the vehicle on the navigation start and end path is determined according to the current positioning information of the vehicle, thereby obtaining the current navigation path interval of the vehicle, and the current road surface type corresponding to the current navigation path interval can be directly read according to the road surface type distribution information.

[0038] Further as an optional implementation, the current road surface material is identified according to the current driving noise, the current driving vibration, and the current road surface image, which specifically includes: S1031, extract the mel frequency cepstral coefficient of the current driving noise, and determine the driving noise feature according to the mel frequency cepstral coefficient; S1032, performing Fourier transform on the current driving vibration to obtain a driving vibration frequency spectrum, and determining a vibration energy distribution feature according to the driving vibration frequency spectrum; S1033, performing image feature extraction on the current road surface image to obtain a road surface texture feature, a road surface color feature and a road surface edge feature; S1034, performing feature fusion on the driving noise feature, the vibration energy distribution feature, the road surface texture feature, the road surface color feature and the road surface edge feature to obtain multi-modal feature data; S1035, inputting the multi-modal feature data into a pre-trained road surface material recognition model to obtain the current road surface material.

[0039] Specifically, the embodiment of the present application extracts a driving noise feature based on the current driving noise, extracts a vibration energy distribution feature based on the current driving vibration, extracts a road surface texture feature, a road surface color feature and a road surface edge feature based on the current road surface image, and then identifies the current road surface material based on the multi-modal feature and the pre-trained road surface material recognition model. The extraction process of the multi-modal feature data is as follows: 1. Acoustic feature extraction: mel frequency cepstral coefficient (MFCC) 1) Noise signal acquisition: the vehicle-mounted microphone collects driving noise in real time (sampling rate ≥ 16 kHz), and a high-pass filter is used to eliminate engine interference.

[0040] 2) Preprocessing and framing: pre-emphasis (H(z)=1-0.97z -1 ) to compensate for high-frequency attenuation, frame according to 20-40 ms (50% overlap), and add Hamming window to reduce spectral leakage.

[0041] 3) MFCC generation process: framed signal→FFT spectrum→mel filter bank→log energy→DCT transform→MFCC coefficient, wherein the mel filter bank maps the linear frequency to the mel scale (m=2595log 10 (1+f / 700)) through 20-40 triangular filters, takes the logarithm of the filter energy, and then performs DCT transform, and retains the first 12-20 coefficients to represent the noise spectrum envelope.

[0042] 2. Vibration feature extraction: frequency energy distribution 1) Vibration signal acquisition: accelerometer (XYZ three-axis) collects chassis vibration (sampling rate ≥ 1 kHz), and band-pass filter (0.5-200 Hz) is used to eliminate high-frequency noise.

[0043] 2) Spectrum energy analysis: After frame windowing, FFT is performed to calculate the amplitude spectrum |X(f)|.

[0044] 3) Key features: Peak frequency distribution, band energy ratio (low frequency (0-20 Hz) / medium frequency (20-100 Hz) energy ratio) and spectrum entropy (reflecting vibration complexity H = -∑p(f)log2p(f)): 3. Visual feature extraction: Triple analysis of road surface image 1) Image preprocessing: The vehicle-mounted camera captures the ROI area of the road surface, and performs grayscale, histogram equalization, and Gaussian filter denoising.

[0045] 2) Multi-dimensional feature extraction Texture features: LBP (Local Binary Pattern) calculates the gradient distribution of the pixel neighborhood; GLCM (Gray Level Co-occurrence Matrix) extracts contrast, energy, and homogeneity.

[0046] Color features: HSV space histogram (H channel sensitive to distinguish asphalt / soil); dominant color clustering (K-means to extract dominant colors).

[0047] Edge features: Canny operator edge detection; Hough transform quantifies the complexity of straight / curved structures.

[0048] 4. Multimodal feature fusion strategy: Normalize the dimension difference by Z-score, then concatenate the feature vectors or perform attention weighting (dynamically allocate weights through the gating mechanism), and finally perform dimension reduction (PCA or autoencoder compression to 50-80 dimensions).

[0049] Further as an optional implementation, the road surface material identification model is trained by the following steps: S201, obtaining sample driving noise, sample driving vibration and sample road surface image under historical driving scene; S202, extracting driving noise feature samples according to the sample driving noise, extracting vibration energy distribution feature samples according to the sample driving vibration, and extracting road surface texture feature samples, road surface color feature samples and road surface edge feature samples according to the sample road surface image; S203, constructing training samples according to the driving noise feature samples, vibration energy distribution feature samples, road surface texture feature samples, road surface color feature samples and road surface edge feature samples, and determining the road surface material labels corresponding to the training samples through artificial labeling; S204, inputting the training samples into the pre-constructed multimodal convolutional neural network to obtain the road surface material identification result; S205, determining the loss value according to the road surface material identification result and the road surface material label; S206. Updating the parameters of the multimodal convolutional neural network using a back propagation algorithm according to the loss value to obtain a trained road material recognition model; Among them, the pavement material label is a secondary label under the pavement type.

[0050] Specifically, sample driving noise, sample driving vibration and sample road surface images in historical driving scenarios are obtained. Based on the same feature extraction method as mentioned above, driving noise feature samples, vibration energy distribution feature samples, road surface texture feature samples, road surface color feature samples and road surface edge feature samples are extracted. Feature fusion is performed on them to obtain training samples. At the same time, the road surface material labels corresponding to the training samples are determined through manual annotation to obtain the training data set.

[0051] The training samples are input into a pre-built multimodal convolutional neural network to obtain the pavement material recognition results. The loss value is determined based on the preset loss function according to the pavement material recognition results and the pavement material label. The parameters of the multimodal convolutional neural network are updated through the back propagation algorithm according to the loss value until the preset convergence conditions are reached (such as the loss value is lower than the preset threshold, the number of iterations reaches the preset threshold, etc.), and a trained pavement material recognition model can be obtained.

[0052] It should be noted that the pavement material labels in the embodiment of the present invention are secondary labels for different pavement types (asphalt pavement, cement pavement, gravel pavement, and soil pavement), and are specifically configured as follows: 1. Asphalt pavement: With asphalt as the binder, it is suitable for roads of all traffic levels and is mainly flexible. The multiple pavement material labels corresponding to asphalt pavement include: 1) Hot Mix Asphalt (HMA) Material: A mixture of high-temperature mixed asphalt and mineral aggregate (crushed stone, sand, etc.).

[0053] Application: high-load scenarios such as highways and airport runways.

[0054] 2) Warm mix asphalt concrete (WMA) Material: Asphalt mixture with zeolite / emulsifier added to lower the mixing temperature.

[0055] Application: Environmentally friendly construction, paving in cold areas.

[0056] 3) Emulsified asphalt gravel Material: cold mix of emulsified asphalt and graded crushed stone.

[0057] Application: Surface layer or repair of highways below grade three.

[0058] 4) Asphalt penetration Material: Asphalt is poured in layers and the joints are filled with compacted gravel.

[0059] Application: Secondary road, branch road (secondary high-grade pavement).

[0060] 5) Asphalt surface treatment Material: Thin layer of asphalt coated aggregate.

[0061] Application: Branch road surface layer or non-slip layer.

[0062] 2. Cement pavement: with cement concrete as the core, rigid structure, high flexural tensile strength. The corresponding multiple pavement material labels of cement pavement include: 1) Ordinary cement concrete Material: Cement, sand, and gravel mixed.

[0063] Application: High-speed, primary road surface layer (high-grade pavement).

[0064] 2) Reinforced concrete Material: Reinforced concrete with embedded steel mesh reinforcement.

[0065] Application: Heavy load roads or areas with large temperature differences.

[0066] 3) Continuous reinforced concrete Material: Longitudinal continuous steel reinforcement concrete.

[0067] Application: High-grade highway with reduced joints.

[0068] 4) Steel fiber reinforced concrete Material: Steel fiber to improve crack resistance.

[0069] Application: Airport runway, bridge pavement.

[0070] 3. Gravel pavement: intermediate or low-grade pavement, low cost but weak durability. The corresponding multiple pavement material labels of gravel pavement include: 1) Graded gravel (pebble) Material: Natural pebbles or gravel compacted according to grading.

[0071] Application: County and township roads (intermediate pavement).

[0072] 2) Mud-bound gravel Material: Clay fills gravel voids.

[0073] Application: Low traffic branch road.

[0074] 3) Water-bound gravel Material: Gravel layer sprinkled with water and compacted.

[0075] Application: Temporary road or base layer.

[0076] 4) Semi-regular stone blocks Material: Artificially carved and polished stone paving.

[0077] Application: Landscape roads or secondary highways.

[0078] 4. Soil pavement: Low-grade pavement that relies on local materials for improvement. Gravel pavement corresponds to multiple pavement material tags including: 1) Granular reinforcement soil Material: Gravel mixed with compacted clay.

[0079] Application: Class IV highway or rural roads.

[0080] 2) Lime / cement stabilized soil Material: Lime or cement-stabilized clay.

[0081] Application: Subbase or low-grade pavement base.

[0082] 3) Industrial waste residue mixture Material: Coal slag, slag, etc. mixed with soil.

[0083] Application: Environmentally friendly rural roads.

[0084] It can be appreciated that the road material recognition model of the embodiment of the present invention can identify specific road materials under different road types, thereby facilitating the subsequent matching of an appropriate engine speed threshold according to the road material and driving speed.

[0085] As a further optional embodiment, a first speed threshold is determined based on the current driving speed and the current road material, and a speed limit control is performed on the engine of the target vehicle based on the first speed threshold, which specifically includes: S1041: Obtain a speed threshold mapping table pre-calibrated through experiments; S1042: Query a speed threshold mapping table based on the current driving speed and the current road surface material to obtain a first speed threshold; S1043: Maintain the speed limit value of the engine of the target vehicle at a first speed threshold.

[0086] Specifically, when the current road surface material identified is consistent with the current road surface type determined based on the road surface type distribution information (e.g., the current road surface material is reinforced concrete, and the current road surface type is cement road surface), it indicates that the road surface material identified in real time is consistent with the road surface type stored in the cloud. It can be determined that the vehicle is now stably traveling on a road of corresponding road surface type and corresponding road surface material. At this time, a pre-constructed speed threshold mapping table is queried based on the current driving speed and the current road surface material to obtain a first speed threshold. The vehicle engine is speed-limited based on the first speed threshold to avoid excessive noise and vibration caused by excessive vehicle engine speed.

[0087] It should be noted that the speed threshold mapping table is pre-calibrated using experimental data. The specific process is as follows: 1) Collect the engine speed threshold T corresponding to the vehicle speed v and the road surface material A in the test scene, which is the maximum engine speed that produces noise / vibration not exceeding a preset value (does not affect the driving experience of the passengers in the vehicle); 2) Construct a multi-element mapping relationship { (v, A), T} according to the vehicle speed v, the road surface material A and the engine speed threshold T; 3) Change the test conditions to obtain multiple sets of multi-element mapping relationships, and form a speed threshold mapping table according to the multiple sets of multi-element mapping relationships { (v, A), T}.

[0088] In some optional embodiments, different speed threshold mapping tables can be selected based on different user modes. Specifically, speed threshold mapping tables for different modes such as static enjoyment mode, normal mode and high-speed mode are constructed in advance. The user's expectations for noise and vibration of the vehicle driving are different in different modes. For example, the user expects the noise and vibration of the vehicle driving to be the smallest in the static enjoyment mode, and the user expects the vehicle speed to be prioritized in the high-speed mode, and the noise and vibration of the vehicle driving can be tolerated. Therefore, the speed threshold mapping table for different user modes can be constructed in advance. After determining the current driving speed and the current road surface material, the corresponding speed threshold mapping table is queried in combination with the mode selected by the user to obtain the engine speed threshold that best meets the user's demand.

[0089] Further as an optional implementation, the second speed threshold is determined according to the current driving speed and the current road surface material, and the speed limit value of the engine of the target vehicle is controlled to gradually increase / decrease according to the preset change rate until the speed limit value of the engine of the target vehicle reaches the second speed threshold, which specifically includes: S1051, obtaining a speed threshold mapping table calibrated in advance through experiments; S1052, querying the speed threshold mapping table according to the current driving speed and the current road surface material to obtain a second speed threshold; S1053, controlling the speed limit value of the engine of the target vehicle to gradually increase / decrease according to the change rate until the second speed threshold is reached.

[0090] Specifically, when the current road surface material identified does not conform to the current road surface type determined according to the road surface type distribution information (for example, the current road surface material is emulsified asphalt macadam, and the current road surface type is cement pavement), it means that the real-time identified road surface material is inconsistent with the road surface type stored in the cloud. It can be determined that the vehicle is currently driving in the transition zone where the road surface type changes. At this time, the speed threshold mapping table constructed in advance is queried according to the current driving speed and the current road surface material to obtain a second speed threshold. The speed limit of the vehicle engine is controlled according to the preset change rate until the speed limit value reaches the second speed threshold, so as to avoid that the change amplitude of the vehicle engine speed is too large to affect the stability of the vehicle.

[0091] It should be noted that the rate of change of the engine speed limit value is calibrated through multiple experiments in advance, so that the impact on the smoothness of the vehicle driving does not exceed 30%.

[0092] The method steps of the embodiments of the present application are described above. It can be recognized that the embodiments of the present application determine the current road surface type based on the road surface type distribution information returned by the cloud server, determine the current road surface material based on the current driving noise, the current driving vibration and the current road surface image, determine that the vehicle is stably driving on the road corresponding to the road surface type and the road surface material when the current road surface material conforms to the current road surface type, determine the first speed threshold value according to the current driving speed and the current road surface material at this time, and control the speed of the engine of the vehicle according to the first speed threshold value to avoid excessive noise and vibration generated by the excessive speed of the engine of the vehicle. When the current road surface material does not conform to the current road surface type, it is determined that the vehicle is driving in the transition zone where the road surface type changes, the second speed threshold value is determined according to the current driving speed and the current road surface material at this time, and the speed of the engine of the vehicle is controlled according to the preset change rate until the speed limit value reaches the second speed threshold value to avoid excessive speed change of the engine of the vehicle affecting the smoothness of the vehicle. The embodiments of the present application can match the corresponding engine speed threshold value according to the driving speed and the road surface material, thereby limiting the speed of the engine of the range extended electric vehicle, reducing the noise and vibration generated by the engine during driving, and improving the driving experience of the driver and passengers.

[0093] Referring to Figure 2 , the embodiments of the present application provide a range extended electric vehicle engine speed limiting device, comprising: A cloud interaction module is configured to obtain a navigation start-end path of a target vehicle, upload the navigation start-end path to a cloud server, and make the cloud server return road surface type distribution information corresponding to the navigation start-end path. A road surface type determination module is configured to determine current positioning information of the target vehicle, and determine a current road surface type according to the current positioning information and the road surface type distribution information. A road surface material identification module is configured to obtain a current driving speed, a current driving noise, a current driving vibration and a current road surface image of the target vehicle, and identify a current road surface material according to the current driving noise, the current driving vibration and the current road surface image. A first speed limiting module is configured to determine a first speed threshold value according to the current driving speed and the current road surface material when the current road surface material conforms to the current road surface type, and control the speed of the engine of the target vehicle according to the first speed threshold value. The second rotating speed limiting module is configured to determine a second rotating speed threshold according to the current driving speed and the current road material when the current road material does not conform to the current road type, and to perform rotating speed limiting change control on the engine of the target vehicle according to a preset change rate until the rotating speed limiting value of the engine of the target vehicle reaches the second rotating speed threshold.

[0094] It can be understood that the contents in the above method embodiments are all applicable to the device embodiments, the device embodiments specifically realize the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0095] With reference to Figure 3 The present application provides an electronic device, comprising: at least one processor; at least one memory for storing at least one program; When the above at least one program is executed by the above at least one processor, the above at least one processor realizes the above method for limiting the rotating speed of the engine of the extended-range electric vehicle.

[0096] It can be understood that the contents in the above method embodiments are all applicable to the device embodiments, the device embodiments specifically realize the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0097] The present application provides an electronic device, comprising:

[0098] The computer readable storage medium of the present application embodiment can execute the method for limiting the rotating speed of the engine of the extended-range electric vehicle provided by the method embodiments of the present application, can execute any combination of the method embodiments to implement the steps, and has the corresponding functions and beneficial effects of the method.

[0099] The present application provides an electronic device, comprising:

[0100] It can be understood that the contents in the above method embodiments are all applicable to the device embodiments, the device embodiments specifically realize the functions of the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0101] Memory, as used in the specification, includes both volatile and nonvolatile memory, and can include but is not limited to volatile memory (e.g., random access memory (RAM)) and non-volatile memory (e.g., read-only memory (ROM)). Additionally, memory can include a storage device, such as a hard disk drive or a solid state drive. Memory can also include removable media, such as a floppy disk, a CD-ROM, a DVD-ROM, a Blu-ray disk, a flash drive, or other like removable media. Memory can also include a database, a database server, or other like database management systems. Memory can include a combination of volatile and nonvolatile memory, as well as removable media. Memory can be located in one computer or distributed across multiple computers. Further, memory can be located in a storage device that is remote from a processor that is accessing the memory. Examples of networks that can connect a processor to remote memory include, but are not limited to, the Internet, an intranet, a local area network, a wide area network, a mobile communications network, and combinations thereof.

[0102] The embodiments described with the present application are intended to be illustrative only and are not limiting of the present application. Those skilled in the art will recognize that the technology described in the embodiments of the present application can be used in a variety of applications and with a variety of devices and systems. The present application is applicable to similar technology and applications.

[0103] The terms "first", "second", "third", "fourth", and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of these terms is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of accomplishing functionalities that are carried out in other embodiments than using the terms first, second, third, and forth. Moreover, the terms "include", "have", and the like, are used in the detailed description and in the claims only to mean "including but not limited to". Additionally, the term "coupled" and variations thereof, as used in the description and claims, are intended to mean connected, although not necessarily directly, and not necessarily mechanically. For example, two elements can be coupled through the use of intermediary elements, other intervening items, or through an electrical, electromagnetic or physical connection.

[0104] In some alternative implementations, the functions / operations described in the block diagrams can occur in a different order than the order described in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality / operations involved. Also, the embodiments presented in the flow diagrams are intended to be illustrative only as many variations of the methods are possible that are not described. The disclosed methods are not limited to the order of operations and logic flow presented in the illustrations. Alternative implementations are possible where the order of the operations is changed and where the sub-operations described as part of a larger operation are performed independently.

[0105] Furthermore, although the present application is described in the context of functional modules, it is to be understood that one or more of the functions and / or features described above can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It will also be appreciated that detailed discussion of the actual implementation of each module is unnecessary to an understanding of the present application. Rather, the actual implementation of the modules, in conjunction with their attributes, functions, and internal relationships, are to be understood within the context of the devices disclosed herein. Thus, those skilled in the art with access to the teachings presented herein will be able to devise suitable implementations of the present application without undue experimentation. It is also to be understood that the particular concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is defined by the appended claims and equivalents thereof.

[0106] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described above in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0107] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logic functions, which can be specifically embodied in any computer readable medium for use by an instruction execution system, device or apparatus, such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from the instruction execution system, device or apparatus, or in conjunction with these instructions execution system, device or apparatus. For the purpose of this specification, "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by an instruction execution system, device or apparatus, or in conjunction with these instruction execution system, device or apparatus.

[0108] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0109] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware which are stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0110] In the above description of the present specification, the description referring to the terms "one embodiment", "another embodiment", or "certain embodiments" or the like means that a specific feature, structure, material or characteristic described in connection with the embodiments or examples is included in at least one embodiment or example of the present application. The illustrative expressions of the above terms do not necessarily refer to the same embodiment or example in the present specification. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0111] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, alternatives and variations to these embodiments can be made without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.

[0112] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the above-described embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope of the present application defined in the claims.

Claims

1. A method for limiting the engine speed of an extended-range electric vehicle, characterized in that: The following steps are involved: Obtaining the navigation start and end paths of the target vehicle, uploading the navigation start and end paths to a cloud server, so that the cloud server returns road type distribution information corresponding to the navigation start and end paths; Determining current positioning information of the target vehicle, and determining a current road surface type based on the current positioning information and the road surface type distribution information; Acquiring a current driving speed, a current driving noise, a current driving vibration, and a current road surface image of the target vehicle, and identifying a current road surface material based on the current driving noise, the current driving vibration, and the current road surface image; When the current road surface material meets the current road surface type, determining a first speed threshold according to the current driving speed and the current road surface material, and performing speed limit control on the engine of the target vehicle according to the first speed threshold; When the current road surface material does not conform to the current road surface type, a second speed threshold is determined based on the current driving speed and the current road surface material, and the speed limit change control of the engine of the target vehicle is performed according to a preset change rate until the speed limit value of the engine of the target vehicle reaches the second speed threshold.

2. The method for limiting engine speed of an extended-range electric vehicle according to claim 1, characterized in that: The step of obtaining the navigation start and end paths of the target vehicle and uploading the navigation start and end paths to a cloud server so that the cloud server returns road type distribution information corresponding to the navigation start and end paths specifically includes: Obtaining the navigation start and end paths through the vehicle system, and uploading the navigation start and end paths to the cloud server; Determining, by the cloud server, a plurality of navigation sections according to the navigation start and end paths, and determining, according to a preset road network database carrying road types, a road surface type label corresponding to each of the navigation sections; determining path interval information corresponding to the navigation segment according to the starting point coordinates and the ending point coordinates of the navigation segment, dividing the navigation start and end paths into a plurality of navigation path intervals according to the path interval information, and labeling each of the navigation path intervals according to the road surface type label to generate the road surface type distribution information; Returning the road type distribution information to the vehicle system via the cloud server; The road surface type labels include asphalt road surface, cement road surface, gravel road surface and soil road surface.

3. The method for limiting the engine speed of an extended-range electric vehicle according to claim 2, characterized in that: The determining of the current road surface type according to the current positioning information and the road surface type distribution information specifically includes: Determining a current navigation path interval of the target vehicle according to the current positioning information and the navigation start and end paths; The current road surface type corresponding to the current navigation path section is determined according to the road surface type distribution information.

4. The method for limiting engine speed of an extended-range electric vehicle according to claim 1, characterized in that: The obtaining of the current road surface material according to the current driving noise, the current driving vibration and the current road surface image recognition specifically includes: extracting Mel-frequency cepstral coefficients of the current driving noise, and determining driving noise characteristics according to the Mel-frequency cepstral coefficients; Performing Fourier transform on the current driving vibration to obtain a driving vibration spectrum, and determining a vibration energy distribution feature based on the driving vibration spectrum; Performing image feature extraction on the current road surface image to obtain road surface texture features, road surface color features, and road surface edge features; performing feature fusion on the driving noise feature, the vibration energy distribution feature, the road surface texture feature, the road surface color feature, and the road surface edge feature to obtain multimodal feature data; The multimodal feature data is input into a pre-trained road surface material recognition model to obtain the current road surface material.

5. The method for limiting engine speed of an extended-range electric vehicle according to claim 4, characterized in that: The road material recognition model is trained by the following steps: Obtain sample driving noise, sample driving vibration, and sample road surface images under historical driving scenarios; Extracting a driving noise feature sample based on the sample driving noise, extracting a vibration energy distribution feature sample based on the sample driving vibration, and extracting a road surface texture feature sample, a road surface color feature sample, and a road surface edge feature sample based on the sample road surface image; constructing training samples based on the driving noise feature samples, the vibration energy distribution feature samples, the road surface texture feature samples, the road surface color feature samples, and the road surface edge feature samples, and determining the road surface material labels corresponding to the training samples through manual annotation; Inputting the training samples into a pre-built multimodal convolutional neural network to obtain a road material recognition result; Determine a loss value based on the road surface material identification result and the road surface material label; Updating the parameters of the multimodal convolutional neural network through a back-propagation algorithm according to the loss value to obtain the trained road material recognition model; The pavement material label is a secondary label under the pavement type.

6. A method for limiting engine speed of an extended-range electric vehicle according to any one of claims 1 to 5, characterized in that: The determining of a first speed threshold according to the current driving speed and the current road surface material, and performing speed limit control on the engine of the target vehicle according to the first speed threshold, specifically includes: Obtaining a speed threshold mapping table pre-calibrated through experiments; querying the speed threshold mapping table according to the current driving speed and the current road surface material to obtain the first speed threshold; The speed limit value of the engine of the target vehicle is maintained at the first speed threshold.

7. A method for limiting engine speed of an extended-range electric vehicle according to any one of claims 1 to 5, characterized in that: The determining of the second speed threshold value based on the current driving speed and the current road surface material, and performing speed limit change control on the engine of the target vehicle according to a preset change rate until the speed limit value of the engine of the target vehicle reaches the second speed threshold value, specifically includes: Obtaining a speed threshold mapping table pre-calibrated through experiments; querying the speed threshold mapping table according to the current driving speed and the current road surface material to obtain the second speed threshold; The speed limit value of the engine of the target vehicle is controlled to gradually increase / decrease according to the change rate until the second speed threshold is reached.

8. An engine speed limiting device for an extended-range electric vehicle, characterized in that: include: A cloud interaction module is used to obtain the navigation start and end paths of the target vehicle, upload the navigation start and end paths to a cloud server, and enable the cloud server to return road type distribution information corresponding to the navigation start and end paths; a road surface type determination module, configured to determine the current positioning information of the target vehicle, and determine the current road surface type according to the current positioning information and the road surface type distribution information; a road surface material recognition module, configured to obtain the current driving speed, current driving noise, current driving vibration, and current road surface image of the target vehicle, and to identify the current road surface material based on the current driving noise, current driving vibration, and current road surface image; a first speed limiting module, configured to determine a first speed threshold according to the current driving speed and the current road surface material when the current road surface material meets the current road surface type, and perform speed limiting control on the engine of the target vehicle according to the first speed threshold; a second speed limit module, configured to determine a second speed threshold value based on the current driving speed and the current road surface material when the current road surface material does not conform to the current road surface type, and to control a speed limit change of the engine of the target vehicle according to a preset change rate until the speed limit value of the engine of the target vehicle reaches the second speed threshold value.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method for limiting the engine speed of an extended-range electric vehicle as claimed in any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, a method for limiting the engine speed of an extended-range electric vehicle is implemented as claimed in any one of claims 1 to 7.

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