Vehicle energy management method, device and system and vehicle

Abstract

The embodiment of the invention provides a vehicle energy management method, device and system and a vehicle, and the method comprises the steps: obtaining the original driving data of a target region; performing track cleaning and track segmentation processing on the original driving data, and determining at least one track segment; generating a standard path according to the at least one track segment; performing path information analysis on the standard path, and extracting slope mileage information; acquiring a slope change threshold value; determining a gradient change parameter according to the gradient mileage information, and when the gradient change parameter is greater than or equal to a gradient change threshold value, determining at least one energy management position on the standard path; acquiring the real-time position of the vehicle; and when the real-time position is matched with the energy management position, controlling the vehicle terminal to execute a corresponding energy management control instruction so as to adjust power parameters of the vehicle. According to the scheme, the overall energy utilization efficiency of the mine vehicle is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle energy management, in particular to a vehicle energy management method, device, system and vehicle. BACKGROUND

[0002] Currently, the energy management and safety control of mine vehicles mainly rely on real-time or predictive operation of vehicle-mounted sensor data and driver's manual experience. This mode has significant defects: first, in terms of battery management, in the face of complex road conditions of long uphill and long downhill alternation in the mine area, the vehicle is difficult to plan the charging and discharging strategy in advance, which easily leads to battery power loss on uphill and insufficient brake energy recovery on downhill, and frequent charging and discharging cycles exacerbate energy loss. Secondly, in terms of path adaptability, the system cannot dynamically perceive and respond to real-time changes of mine roads, and lacks active warning mechanism for dangerous sections such as steep slopes and sharp turns, which poses a safety hazard. A large amount of vehicle operation data is in isolated state and cannot be aggregated, analyzed and learned through the cloud, so as to realize global energy optimization scheduling at the vehicle fleet level and low overall energy utilization efficiency. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide a vehicle energy management method, device, system and vehicle, which can solve the problem of low overall energy utilization efficiency of mine vehicles.

[0004] Therefore, the embodiments of the first aspect of the present application provide a vehicle energy management method.

[0005] The embodiments of the second aspect of the present application provide a vehicle energy management device.

[0006] The embodiments of the third aspect of the present application provide a vehicle energy management system.

[0007] The embodiments of the fourth aspect of the present application provide a vehicle.

[0008] To achieve the above objectives, an embodiment of the first aspect of the present invention provides a vehicle energy management method for a vehicle energy management system. The vehicle energy management system includes a cloud processing layer and at least one vehicle terminal. The at least one vehicle terminal is communicatively connected to the cloud processing layer to receive energy management control commands issued by the cloud processing layer. The vehicle energy management method is used in the cloud processing layer and includes: acquiring raw driving data of a target area; performing trajectory cleaning and trajectory segmentation on the raw driving data to determine at least one trajectory segment; generating a standard path based on the at least one trajectory segment; performing path information analysis on the standard path to extract slope mileage information; acquiring a slope change threshold; determining a slope change parameter based on the slope mileage information; when the slope change parameter is greater than or equal to the slope change threshold, determining at least one energy management position on the standard path; acquiring the real-time position of the vehicle; and when the real-time position matches the energy management position, controlling the vehicle terminal to execute the corresponding energy management control command to adjust the vehicle's power parameters.

[0009] The vehicle energy management method proposed in this invention is applied to a vehicle energy management system. A vehicle terminal is installed on the vehicle, communicating with the cloud processing layer in the vehicle energy management system to receive energy management control commands from the cloud processing layer. The cloud processing layer acquires raw driving data of vehicles within a target area, cleans and segments it into independent trajectory segments, and uses an adaptive clustering algorithm to generate standard paths representing the actual road network from these trajectory segments. Then, through in-depth analysis of the standard paths, its slope and mileage information are extracted, and combined with preset energy management thresholds and slope change thresholds, specific locations on the path that have a significant impact on energy management are intelligently identified, i.e., energy management locations. Finally, during actual vehicle operation, by matching its real-time location with the cloud-predicted energy management locations, the optimal energy management control command is automatically triggered and executed upon arrival at the energy management location, thereby achieving proactive and precise adjustment of vehicle power parameters and improving the overall energy utilization efficiency of the vehicle.

[0010] In some technical solutions, optionally, the original driving data is subjected to trajectory cleaning and trajectory segmentation processing to determine at least one trajectory segment, including: removing data noise from the original driving data to determine purified trajectory data; obtaining the location information parameters of the loading point and unloading point; and segmenting the purified trajectory data according to the location information parameters to obtain at least one independent round-trip trajectory segment as the processed trajectory segment.

[0011] In this solution, noise such as drift and jumps in the Global Positioning System (GPS) data are removed to obtain purified trajectory data that can truly reflect the continuous movement of the vehicle.

[0012] Furthermore, based on the pre-acquired or learned geographical coordinate parameters of the loading and unloading points, the purified continuous trajectory is intelligently segmented between each time the vehicle leaves the loading point and returns to the loading point, thereby obtaining multiple independent round-trip trajectory segments corresponding to the complete transportation task.

[0013] In some technical solutions, optionally, a standard path is generated based on at least one trajectory segment, including: using an adaptive parameter clustering algorithm to analyze multiple trajectory segments to distinguish different driving paths and merge trajectory segments with the same driving path, thereby extracting a standard driving path; and smoothing the standard driving path to generate a standard path containing a continuous latitude and longitude sequence.

[0014] In this scheme, an adaptive parameter clustering algorithm is used to perform unsupervised machine learning analysis on multiple trajectory segments. This automatically identifies and merges spatially continuous and dense trajectory points into clusters representing the same physical road. Simultaneously, based on the spatial separation characteristics between trajectories, paths leading to different destinations are distinguished, thereby extracting standard driving paths representative of each route. Subsequently, the extracted paths undergo curve smoothing optimization to eliminate local jitter caused by noise in the original data, ultimately generating a high-quality standard path composed of a continuous and accurate latitude and longitude sequence.

[0015] In some technical solutions, optionally, a slope change parameter is determined based on slope mileage information. When the slope change parameter is greater than or equal to the slope change threshold, at least one energy management location on the standard path is determined, including: determining the slope and slope change trend of the trajectory segment based on the slope mileage information; when the slope change trend indicates a continuous uphill or downhill section, and the absolute value of the slope or the slope change rate of the trajectory segment is greater than or equal to the slope change threshold, the starting point of the trajectory segment is determined as the energy management location.

[0016] In this scheme, the slope mileage information of the standard route is first analyzed to identify the absolute value and trend of the slope of the road segment, especially the continuous uphill or downhill sections. Then, by comparing the slope change parameters that characterize the steepness and intensity of the slope with the preset slope change threshold, when the road segment meets both the trend condition and the intensity condition, the starting point of the road segment is accurately marked as an energy management location.

[0017] In some technical solutions, the vehicle energy management method may optionally include: obtaining a safety warning threshold; determining a slope characteristic parameter based on slope mileage information; determining at least one instrument warning location on the standard path when the slope characteristic parameter is greater than or equal to the safety warning threshold; and triggering the corresponding safety warning when the vehicle's real-time location matches the instrument warning location.

[0018] In this scheme, the slope characteristic parameters (such as slope extremes, curve curvature, etc.) of the standard path are analyzed and judged by a preset safety warning threshold. When the parameter reaches or exceeds the safety warning threshold, the corresponding position is determined as the instrument warning position.

[0019] In some technical solutions, the vehicle energy management method may optionally include: obtaining front-end services from the cloud; sending energy management control commands to the vehicle terminal; the vehicle terminal receiving the sent energy management control commands and forwarding them to the vehicle controller via the vehicle control bus; the vehicle controller receiving and storing the energy management control commands; and when the real-time location of the vehicle matches the energy management location in the energy management control command, the vehicle controller executes the corresponding energy management control command or triggers the corresponding safety prompt.

[0020] In this solution, the generated energy management control commands are sent to the vehicle terminal via a cloud-based front-end service. After receiving the data, the vehicle terminal forwards it to the vehicle controller, which serves as the control core, via the onboard control bus. The vehicle controller is responsible for receiving and locally storing the energy management control commands.

[0021] During vehicle operation, the vehicle controller continuously compares the vehicle's real-time location with the energy management location in the energy management control command. Once a match is found, the corresponding energy management control command is automatically executed or a safety warning is triggered.

[0022] In some technical solutions, optionally, corresponding energy management control commands are executed to adjust the vehicle's power parameters, including: dynamically limiting the vehicle's drive motor torque, generator power, or gearbox gear position according to the received energy management control commands.

[0023] In this solution, after receiving the energy management control command from the cloud, the vehicle controller implements dynamic and constrained control over one or more of the three core power parameters of the vehicle—drive motor torque, generator power, and gearbox gear position—through the vehicle control network, in accordance with road conditions and vehicle status.

[0024] A second aspect of the present invention provides a vehicle energy management device, comprising: a data acquisition module for acquiring raw driving data of a target area; a data processing module for performing trajectory cleaning and trajectory segmentation on the raw driving data to determine at least one trajectory segment; a path generation module for generating a standard path based on at least one trajectory segment; an information extraction module for performing path information analysis on the standard path to extract slope and mileage information; a threshold determination module for acquiring a slope change threshold; a feature determination module for determining a slope change parameter based on the slope and mileage information, and determining at least one energy management location on the standard path when the slope change parameter is greater than or equal to the slope change threshold; a location acquisition module for acquiring the real-time location of the vehicle; and an energy management module for controlling the vehicle terminal to execute a corresponding energy management control command to adjust the vehicle's power parameters when the real-time location matches the energy management location.

[0025] An embodiment of the third aspect of this application provides a vehicle energy management system, which includes a cloud processing layer and at least one vehicle terminal. The at least one vehicle terminal is communicatively connected to the cloud processing layer to receive energy management control commands issued by the cloud processing layer.

[0026] An embodiment of the fourth aspect of this application provides a vehicle equipped with a vehicle terminal as described in the third aspect of the vehicle energy management system.

[0027] Additional aspects and advantages of the technical solutions of the present invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0028] Figure 1 One of the flowcharts of the vehicle energy management method according to this application is shown;

[0029] Figure 2 A second schematic flowchart of the vehicle energy management method according to this application is shown;

[0030] Figure 3 A third schematic flowchart of the vehicle energy management method according to this application is shown;

[0031] Figure 4 A fourth schematic flowchart of the vehicle energy management method according to this application is shown;

[0032] Figure 5 Fifth of the flowcharts illustrating the vehicle energy management method according to this application is shown;

[0033] Figure 6 A sixth schematic flowchart of the vehicle energy management method according to this application is shown;

[0034] Figure 7 A schematic block diagram of the structure of a vehicle energy management device according to this application is shown;

[0035] Figure 8 A schematic block diagram of the vehicle energy management system according to this application is shown;

[0036] Figure 9 A schematic block diagram of the structure of the vehicle according to this application is shown;

[0037] Figure 10 A schematic diagram of the structure of a mine vehicle safety and energy management system according to an embodiment of this application is shown.

[0038] Among them, 900: vehicle energy management device; 902: data acquisition module; 904: data processing module; 906: path generation module; 908: information extraction module; 910: threshold determination module; 912: feature determination module; 914: location acquisition module; 916: energy management module; 100: vehicle energy management system; 102: cloud processing layer; 104: vehicle terminal; 200: vehicle. Detailed Implementation

[0039] To better understand the above-described objectives, features, and advantages of the embodiments of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0040] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, embodiments of the invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of this application is not limited to the specific embodiments disclosed below.

[0041] In current mine vehicle operation and management, energy management and driving safety control are primarily based on a localized, reactive, and experience-dependent model. The limitations of this system architecture and methodology constitute the core problem that this technical solution aims to address. The following is a detailed description of this background technology:

[0042] Vehicle energy management strategies (such as power output and regenerative braking) and safety warnings rely entirely on real-time data from onboard sensors (such as slope sensors and battery management systems) and the driver's experience in assessing road conditions. This means that each vehicle is essentially an information silo, only able to react to its immediate local environment, and unable to proactively plan for the overall road conditions it will face.

[0043] If a vehicle is not aware of its approach to a long uphill section, it may enter the climbing phase with a low battery level. This will cause the battery to discharge continuously at high power, which may not only trigger battery protection and limit power, but also accelerate battery capacity degradation due to deep discharge and increase energy consumption per unit distance.

[0044] When facing a long downhill slope, if a vehicle is unaware of this, its initial battery charge may already be high. The electric braking (energy recovery) system will quickly disengage as the battery is fully charged, forcing the mechanical braking system to bear the main braking load. This results in the valuable gravitational potential energy not being recovered as electrical energy, but being wasted as heat energy in the brake pads, and also exacerbates wear on the braking system, creating a safety hazard.

[0045] Under undulating road conditions, due to the passive strategy, the battery often experiences rapid switching between charging and discharging in a short period of time. This irregular operating condition will shorten the battery life and reduce the overall energy utilization efficiency.

[0046] Furthermore, mining roads frequently change due to the progress of mining operations. The existing system lacks the ability to automatically learn and update global road network information, and cannot promptly synchronize changes such as new or closed road sections to vehicles, resulting in a disconnect between strategy and actual conditions.

[0047] For fixed hazardous road sections such as steep slopes, sharp bends, and intersections, the system lacks the ability to provide advance warnings based on precise location. Safety relies entirely on the driver's instantaneous observation and memory, which can easily lead to accidents under conditions such as fatigue or poor weather.

[0048] The vehicle energy management method, device, system, and vehicle provided in this application will be described in detail below with reference to specific embodiments and application scenarios.

[0049] This embodiment provides a vehicle energy management method for a vehicle energy management system. The vehicle energy management system includes a cloud processing layer and at least one vehicle terminal. The at least one vehicle terminal is communicatively connected to the cloud processing layer to receive energy management control commands issued by the cloud processing layer. The vehicle energy management method is used by the cloud processing layer, such as... Figure 1 As shown, vehicle energy management methods include:

[0050] Step S100: Obtain the raw driving data of the target area;

[0051] Step S102: Perform trajectory cleaning and trajectory segmentation on the original driving data to determine at least one trajectory segment;

[0052] Step S104: Generate a standard path based on at least one trajectory segment;

[0053] Step S106: Analyze the path information of the standard path and extract the slope and mileage information;

[0054] Step S108: Obtain the slope change threshold;

[0055] Step S110: Determine the slope change parameter based on the slope mileage information. When the slope change parameter is greater than or equal to the slope change threshold, determine at least one energy management location on the standard path.

[0056] Step S112: Obtain the real-time location of the vehicle;

[0057] Step S114: When the real-time location matches the energy management location, the vehicle control terminal executes the corresponding energy management control command to adjust the vehicle's power parameters.

[0058] The vehicle energy management method proposed in this invention is applied to a vehicle energy management system. A vehicle terminal is installed on the vehicle, communicating with the cloud processing layer in the vehicle energy management system to receive energy management control commands from the cloud processing layer. The cloud processing layer acquires raw driving data of vehicles within a target area, cleans and segments it into independent trajectory segments, and uses an adaptive clustering algorithm to generate standard paths representing the actual road network from these trajectory segments. Then, through in-depth analysis of the standard paths, its slope and mileage information are extracted, and combined with preset energy management thresholds and slope change thresholds, specific locations on the path that have a significant impact on energy management are intelligently identified, i.e., energy management locations. Finally, during actual vehicle operation, by matching its real-time location with the cloud-predicted energy management locations, the optimal energy management control command is automatically triggered and executed upon arrival at the energy management location, thereby achieving proactive and precise adjustment of vehicle power parameters and improving the overall energy utilization efficiency of the vehicle.

[0059] For example, vehicles equipped with vehicle terminals include, but are not limited to, mining vehicles.

[0060] Specifically, the cloud processing layer continuously receives data packets uploaded by one or more vehicle terminals deployed in a target area (such as a specific mine, logistics park, or closed test track) to obtain the raw driving data corresponding to each vehicle.

[0061] Raw driving data includes, but is not limited to: vehicle identification number, Global Positioning System (GPS) sequence, altitude, vehicle speed, battery status, and other unprocessed vehicle source data.

[0062] In addition to the original driving data reported by the vehicle itself, external geographic information system data, high-precision map data, or meteorological data can be introduced to assist in the generation or correction of standard route and slope information, thereby improving the accuracy and robustness of the road spectrum.

[0063] The target area is a pre-selected specific geographical region from which services are needed. The road conditions (gradient, curves, mileage) within the target area are relatively fixed and can be learned. The selection of the target area defines the boundaries of system optimization and path learning. Furthermore, the range and boundaries of the target area can be adjusted at any time.

[0064] The raw driving data undergoes trajectory cleaning and segmentation. First, GPS noise, such as coordinate jumps caused by signal obstruction, is removed. Then, trajectory deviations are corrected by using filtering algorithms (such as Kalman filtering) to make the trajectory smoother and more continuous.

[0065] Then, based on the vehicle's operational logic, the start and end points of each valid operation are identified. For example, in a mining scenario, loading and unloading points are used as key identifiers to divide a long-term trajectory into multiple independent closed loops of "loading point, unloading point, loading point (empty return)," each loop being a trajectory segment. A trajectory segment corresponds to a complete driving process data with a clear start and end point business significance collected by the vehicle terminal.

[0066] The cloud processing layer uses an adaptive parameter clustering algorithm to process a large number of trajectory segments. The adaptive parameter clustering algorithm can automatically merge the same trajectory, classifying trajectory segments from different vehicles, at different times, and in similar spatial locations into the same driving route.

[0067] Furthermore, the cloud processing layer uses an adaptive parameter clustering algorithm to process a large number of trajectory segments, distinguishing different trajectories and separating trajectory segments that lead to different destinations or take different lanes.

[0068] Finally, one or more smooth centerlines that best represent the actual driving route of the vehicle are extracted from at least one trajectory segment, i.e., the standard path.

[0069] A standard path is a digital and standardized representation of the actual physical roads within a target area, obtained by the cloud processing layer through big data learning. A standard path is no longer limited to the trajectory segment corresponding to a specific trip, but can also be the result of fitting multiple trajectory segments.

[0070] The standard route is analyzed for route information, and the slope of the route is calculated based on the latitude, longitude and altitude data of each point on the standard route.

[0071] Specifically, the ratio of the elevation difference between adjacent points to the horizontal distance is calculated to obtain a continuous curve or data sequence of the slope change with mileage along the entire route, i.e., slope mileage information.

[0072] The gradient change threshold is a safety threshold condition that triggers energy management (e.g., "long uphill management," "long downhill management," "steep descent control," etc.) under any road conditions. For example, a threshold condition for the "long uphill management" mode is set where "the gradient is consistently greater than 3% and the length exceeds 500 meters." The gradient change threshold is configured and pre-set based on vehicle performance, battery characteristics, and safety regulations.

[0073] Scan slope mileage information and calculate slope change parameters, including but not limited to the current slope value, the rate of change of slope, and the cumulative mileage of continuous uphill / downhill sections.

[0074] The calculated parameters are compared with a preset slope change threshold. When the slope change parameter is greater than or equal to the slope change threshold, at least one energy management location on the standard path is determined. For example, when the system detects a path that meets the condition of "slope continuously greater than 3% and cumulative mileage exceeding 500 meters", the starting point of the path is determined to be an energy management location.

[0075] The vehicle terminal continuously obtains the vehicle's real-time latitude and longitude coordinates via GPS and periodically reports them to the cloud processing layer, or the vehicle control unit (VCU) directly reads the vehicle's real-time latitude and longitude coordinates locally.

[0076] When the real-time location matches the energy management location, that is, in the cloud or on the vehicle (depending on the architecture design), the reported real-time location is compared with the pre-stored energy management location coordinates.

[0077] When the distance between the real-time location and the energy management location is less than a preset range (e.g., 10 meters), the match is considered successful.

[0078] After successful matching, the cloud sends instructions to the vehicle terminal, or the VCU automatically calls the energy management control instructions bound to that location, and the vehicle terminal then executes the energy management control instructions.

[0079] For example, by increasing engine power or limiting discharge current at the beginning of a long uphill slope, and by shifting to a high energy recovery gear and limiting the maximum vehicle speed at the beginning of a long downhill slope, precise and forward-looking adjustments can be made to power parameters such as motor torque, generator power, and gearbox gear, thereby improving the overall energy efficiency of the vehicle.

[0080] Understandably, by identifying energy management locations along long uphill / downhill sections in advance, the vehicle's energy management system can proactively allocate energy. Maintaining high charge or increasing torque before going uphill avoids battery depletion during the journey; reserving charging capacity before going downhill maximizes energy recovery. This significantly reduces the battery's peak load and charging / discharging frequency, thereby improving overall vehicle energy utilization and extending battery cycle life.

[0081] In some embodiments, the energy management threshold and the gradient change threshold may optionally be dynamically adjustable thresholds. For example, the gradient or mileage threshold that triggers energy management may be dynamically adjusted based on real-time battery temperature, vehicle load, or driver behavior.

[0082] In some embodiments, the vehicle energy management method can be optionally extended to vehicle platooning. The cloud processing layer generates a collaborative energy management strategy for the lead vehicle or the entire fleet, taking into account factors such as vehicle spacing and aerodynamics to achieve global energy saving at the fleet level.

[0083] Furthermore, the vehicle energy management method can be extended to vehicles with different power configurations (such as pure electric, hybrid, and fuel cell vehicles), and clearly defines how the strategies issued from the cloud are integrated with the vehicle's local underlying control logic and how responsibilities are allocated.

[0084] In some embodiments, optionally, such as Figure 2 As shown, the original driving data undergoes trajectory cleaning and segmentation processing to determine at least one trajectory segment, including:

[0085] Step S1020: Remove noise from the original driving data to determine the purified trajectory data;

[0086] Step S1022: Obtain the location information parameters of the loading point and unloading point;

[0087] Step S1024: Segment the purification trajectory data according to the location information parameters to obtain at least one independent round-trip trajectory segment as the processed trajectory segment.

[0088] In this embodiment, noise such as drift and jumps in the GPS data are removed to obtain purified trajectory data that can truly reflect the continuous movement of the vehicle.

[0089] Furthermore, based on the pre-acquired or learned geographical coordinate parameters of the loading and unloading points, the purified continuous trajectory is intelligently segmented between each time the vehicle leaves the loading point and returns to the loading point, thereby obtaining multiple independent round-trip trajectory segments corresponding to the complete transportation task.

[0090] Understandably, the chaotic raw data stream is automatically transformed into clean, complete basic analytical units with clear business semantics, laying an accurate and reliable data foundation for subsequent path clustering, feature extraction, and energy strategy generation.

[0091] Specifically, the cloud processing layer uses algorithms to filter and correct the received raw driving data (mainly GPS trajectory point sequences).

[0092] Identify noise in the raw driving data. Noise includes: drift points: irregular small-range jumps in GPS coordinates when the vehicle is stationary or moving at low speed; jump points: instantaneous large jumps in coordinates that far exceed the actual moving ability of the vehicle due to the satellite signal being briefly blocked (such as passing through a tunnel or approaching large equipment) and then being recaptured (for example, suddenly jumping from point A to point B, which is 100 meters away, and then immediately jumping back).

[0093] Noise removal in the cloud processing layer: Algorithm rules are used for filtering, including but not limited to: Threshold filtering based on speed and distance: Calculate the instantaneous speed between adjacent trajectory points. If the speed exceeds the vehicle's physical limit (e.g., 200 km / h), the latter point is determined to be a jump noise and is removed; Trajectory smoothing algorithm: Use algorithms such as Kalman filtering and mean filtering to smooth the position of the current point according to the reasonable trend of the preceding and following trajectory points, and suppress small-range drift.

[0094] After noise removal through the cloud processing layer, the purified trajectory data is obtained, which is a GPS coordinate sequence that more accurately reflects the vehicle's continuous and smooth driving path after the aforementioned noise removal processing. The purified trajectory data is the data foundation for subsequent accurate analysis.

[0095] The system administrator pre-marks the geographical coordinates (e.g., a polygonal fence or a circle with a center and radius) of loading points (such as below the electric shovel or in the loader's operating area) and unloading points (such as the crushing station entrance or the spoil heap) on the cloud map based on the actual layout of the mining area.

[0096] Alternatively, the cloud processing layer can automatically identify potential loading and unloading points by analyzing historical data of clusters of vehicles moving at low speeds or remaining stationary for extended periods, and generate their location parameters.

[0097] Location information parameters are quantitative data used to define the geographical areas of loading and unloading points, such as the latitude and longitude of the center point, the radius of the area, or the set of coordinates of the polygon vertices. Location information parameters provide the logical basis for segmenting the trajectory.

[0098] Based on location information parameters, it is determined when a vehicle enters or leaves the loading or unloading point area, thereby cutting the long-term continuous data stream into independent trajectory segments.

[0099] When the cloud processing layer detects that a vehicle has entered the loading point area and stayed for a sufficient time (to complete loading) before driving out, this point is recorded as the start of the journey. Subsequently, the vehicle travels to the unloading point area, stays, and completes unloading before driving out, and this point is recorded. The vehicle may return to the loading point area empty to prepare for the next loading.

[0100] When the vehicle re-enters the loading area, it marks the end of a complete work cycle.

[0101] A round-trip trajectory segment refers to the trajectory data corresponding to a complete work cycle starting from the loading point, through driving, unloading, empty return, and finally arriving at the loading point again. The round-trip trajectory segment encapsulates a complete transportation task and is the basic logical unit for subsequent standard route generation and energy consumption analysis.

[0102] By removing noise from the data, the quality of the raw data is greatly improved, avoiding path distortion and slope calculation errors caused by GPS mistakes. This ensures the accuracy and reliability of all subsequent analyses (such as path generation and slope extraction). This is a prerequisite for the entire big data analysis process to produce effective results.

[0103] Furthermore, by introducing the business logic of loading and unloading points for segmentation, the meaningless, long-spanning raw data stream is transformed into round-trip trajectory segments with clear business meaning. This allows subsequent analysis (such as path clustering and energy consumption statistics) to be conducted on a per-transportation-task basis, making the analysis results more practically instructive. For example, it can accurately calculate the average energy consumption of a specific route and the optimal energy management strategy.

[0104] In addition to noise removal based on position jumps, cleaning rules based on the fusion of multi-sensor information such as vehicle speed, heading angle, and time interval can be introduced. For example, the GPS speed can be verified by combining the actual vehicle speed on the Controller Area Network (CAN) bus to identify and filter data points that do not conform to physical laws.

[0105] In some embodiments, optionally, the vehicle's load status (empty / heavy load) can be correlated during cleaning. The vehicle's trajectory characteristics (such as turning radius and acceleration / deceleration performance) differ under heavy load, and different trajectory smoothing models can be established accordingly to make the cleaning trajectory data more reflective of the vehicle's true center of mass movement path.

[0106] In some embodiments, optionally, in addition to relying on fixed loading and unloading point location information parameters to segment the trajectory, the trajectory segmentation rules also include: identifying areas where the vehicle moves at low speed or remains stationary for a long time (e.g., more than 2 minutes) at any location, and automatically marking them as potential work points or waiting points as the basis for segmentation; or, obtaining the cargo box lifting status, engine idling speed signal, etc. through the vehicle terminal to accurately determine the time when loading and unloading actions occur, and using this as the segmentation point, which is more accurate than simply relying on location.

[0107] Based on this, the round-trip trajectory segment is further subdivided into a heavy-load segment (i.e., traveling from the loading point to the unloading point) and an unloaded segment (i.e., traveling from the unloading point to the loading point). Because the two differ greatly in energy consumption, vehicle speed, and braking strategies, processing them separately can provide a basis for generating more targeted energy management strategies in the future.

[0108] In some embodiments, optionally, such as Figure 3 As shown, a standard path is generated based on at least one trajectory segment, including:

[0109] Step S1040: An adaptive parameter clustering algorithm is used to analyze multiple trajectory segments to distinguish different driving paths and merge trajectory segments with the same driving path, thereby extracting the standard driving path.

[0110] Step S1042: Smooth the standard driving path to generate a standard path containing a continuous latitude and longitude sequence.

[0111] In this embodiment, an adaptive parameter clustering algorithm is used to perform unsupervised machine learning analysis on multiple trajectory segments. This automatically identifies and merges spatially continuous and dense trajectory points into clusters representing the same physical road. Simultaneously, based on the spatial separation characteristics between trajectories, paths leading to different destinations are distinguished, thereby extracting standard driving paths representative of each route. Subsequently, the extracted paths undergo curve smoothing optimization to eliminate local jitter caused by noise in the original data, ultimately generating a high-quality standard path composed of a continuous and accurate latitude and longitude sequence.

[0112] For example, adaptive parameter clustering algorithms specifically refer to a class of unsupervised machine learning algorithms that do not require pre-specifying the number of clusters and can automatically adjust the judgment threshold based on the data's own distribution density. Examples include density-based spatial clustering of applications with noise (DBSCAN) or its variants. Its adaptability is reflected in the algorithm's ability to automatically determine the distance threshold and minimum number of points required to cluster trajectory points into a road within a geographical area, rather than using fixed parameters, thus adapting to the uneven density of roads in mining areas.

[0113] Clustering algorithms with adaptive parameters can identify trajectories heading to different destinations or traveling in different lanes. For example, two sets of trajectories starting from the same loading point and heading to "Crushing Station No. 1" and "Dumping Yard No. 2" respectively, although their initial parts overlap, will be classified as two different clusters, i.e., two different travel paths, after the bifurcation point because their spatial distance exceeds the adaptive density threshold.

[0114] For multiple trajectories heading to the same destination, generated by different vehicles or different shifts, even if they have slight lateral deviations due to driver habits or GPS errors, the algorithm will identify them as the same cluster because they are spatially continuous and densely distributed. The algorithm then calculates the centerline of this cluster.

[0115] The centerline is the extracted standard driving path. The standard driving path is not limited to any single actual driving record, but can also be an optimal or average path learned from massive historical data that best represents the general driving situation of the route.

[0116] However, the standard driving path directly generated from the clustering algorithm may be composed of a series of discrete points, and there may be minor jagged edges or unnecessary turns in some places due to fluctuations in the original data.

[0117] Therefore, the standard driving path is smoothed to generate a standard path containing a continuous sequence of latitude and longitude. Curve smoothing algorithms (such as Bézier curve fitting, spline interpolation, or moving average filtering) are then applied to optimize the path shape. The goal is to eliminate local jitter and burrs on the path while maintaining the overall road orientation and feature points (such as curve vertices), making the path curve smoother and more continuous, conforming to the dynamic characteristics of smooth vehicle steering.

[0118] The final output after smoothing is a digital path composed of ordered, continuous latitude and longitude coordinates—a standard path containing a continuous sequence of latitude and longitude coordinates. This sequence serves as the unique and reliable geometric basis for subsequent accurate mileage calculations, slope analysis, and key point localization. The standard path is a consensus trajectory for at least one vehicle traveling on a road segment.

[0119] During the smoothing process, a curve (such as a spiral curve) that meets the vehicle's minimum turning radius constraint is used for fitting to ensure that the generated standard path is not only geometrically smooth, but also safe and comfortable for the vehicle to drive in practice.

[0120] Understandably, the generated standard path possesses high accuracy, high smoothness, and high consistency. High accuracy ensures accurate position matching; high smoothness makes the calculated parameters such as slope and curvature more realistic, avoiding erroneous control commands caused by trajectory noise; high consistency ensures that all vehicles follow the same digital route book on the same road segment, enabling unified optimization management at the fleet level. This is a prerequisite for achieving accurate predictive energy management and safety alerts.

[0121] The adaptive parameters depend not only on the spatial density of the trajectory points, but also on other adaptive factors, such as:

[0122] Based on vehicle characteristics: The clustering radius is dynamically adjusted according to the vehicle type (e.g., wide-body vehicle, regular truck) and load status of the generated trajectory. Heavy-duty vehicles have more stable trajectories, so the clustering radius can be smaller; empty vehicles may be more flexible in changing lanes, so the clustering radius can be appropriately widened.

[0123] Based on trajectory quality: A confidence score (based on its GPS accuracy and smoothness) is calculated for each trajectory segment, and higher confidence trajectories are given higher weights during clustering, making the generated standard paths more reliable.

[0124] Furthermore, in addition to the GPS trajectories reported by vehicle terminals, the input for cluster analysis can include path data from other sources. These include, but are not limited to: vector paths from pre-imported mining area design drawings and initial road models generated by UAV surveying. The algorithm can be designed to use this data as constraints to perform cluster fusion, effectively combining historical experience data with prior map data, thereby accelerating the construction speed and accuracy of the initial road network.

[0125] In some embodiments, optionally, such as Figure 4 As shown, slope change parameters are determined based on slope mileage information. When the slope change parameter is greater than or equal to the slope change threshold, at least one energy management location on the standard path is determined, including:

[0126] Step S1100: Determine the slope and slope change trend of the trajectory segment based on the slope mileage information;

[0127] Step S1102: When the slope change trend indicates a continuous uphill or downhill section, and the absolute value of the slope or the slope change rate of the trajectory segment is greater than or equal to the slope change threshold, the starting point of the trajectory segment is determined as the energy management location.

[0128] In this embodiment, the slope mileage information of the standard route is first analyzed to identify the absolute value and trend of the slope of the road segment, especially the continuous uphill or downhill sections. Then, by comparing the slope change parameters that characterize the steepness and intensity of the slope with the preset slope change threshold, when the road segment meets both the trend condition and the intensity condition, the starting point of the road segment is accurately marked as an energy management location.

[0129] Understandably, this process transforms complex road conditions into quantifiable and predictable rules, enabling the pre-setting of control command trigger points on the digital path before the vehicle reaches the physical ramp, thus providing crucial decision-making basis for forward-looking and precise energy management.

[0130] Specifically, the slope mileage information associated with the standard route, namely a sequence of slope values ​​arranged in mileage order, is scanned and analyzed.

[0131] A trajectory segment refers to any continuous section of a standard path. The cloud processing layer can calculate the average slope, maximum slope, or representative slope of any continuous section of the path.

[0132] The undulation pattern of a path is determined by analyzing the sequence of slope values. The core trends include: a continuous uphill trend: within a section of the path, the slope value remains positive and the overall trend is upward or maintained at a high level; and a continuous downhill trend: within a section of the path, the slope value remains negative and the overall trend is downward or maintained at a low level.

[0133] Fluctuation trend: The slope value alternates between positive and negative, without a significant and continuous rise or fall.

[0134] For example, the judgment logic is usually based on sliding window analysis. For instance, if more than 90% of the sampling points have a slope value greater than 0 within a window of a preset length (e.g., 200 meters) and the overall cumulative elevation increases significantly, then the segment is judged to have a continuous uphill trend.

[0135] First, the route segment must be identified as having a continuous uphill or downhill trend. This filters out short, gentle slopes or undulating sections, ensuring that only long slopes that truly impact overall energy planning are managed.

[0136] In addition to meeting the trend condition, one of the following intensity conditions must also be met:

[0137] Absolute gradient ≥ threshold: The average gradient of the road segment or the gradient at key points must reach or exceed a set value (e.g., gradient ≥ 3%). This ensures that the steepness of the gradient is sufficient to have a substantial impact on the vehicle's energy state.

[0138] Slope change rate ≥ threshold: At the starting point of a slope, the rate of change of the slope that increases sharply over a short distance (slope change per unit distance) must reach a threshold. This is used to identify steep slope starting points; even if the overall slope length is not long, a very steep start requires advance management.

[0139] The gradient change threshold is a set of preset key parameters used to trigger energy management. It is not a single value, but a set of thresholds that may include a minimum sustained gradient length threshold, a minimum absolute gradient threshold, and a minimum gradient change rate threshold. These thresholds can be configured based on vehicle performance (such as power and weight) and battery characteristics.

[0140] When a road segment simultaneously meets the above trend and intensity conditions, the cloud processing layer will officially mark the starting point of the road segment (marked with precise latitude and longitude coordinates) as an energy management location so that pre-adjustment can be initiated before vehicles enter the road segment.

[0141] By identifying the energy management location and its accompanying trend information (whether it's a continuous uphill or downhill slope), the system can pre-install the most suitable, refined control command package for each location. For example, at the start of a long uphill climb, the command might be to limit the highest gear and increase torque reserve; at the start of a steep downhill climb, the command might be to switch to a high energy recovery gear and activate the retarder pre-lubrication. This represents a leap from coarse, simplistic reactive control to refined, scenario-customized predictive control, addressing the shortcomings of battery management.

[0142] In some embodiments, optionally, the real-time or historical road surface condition (e.g., dry, wet, muddy) of the associated road segment can be considered during the determination. The same gradient may require more gentle torque control and earlier braking preparation on a wet surface, thus the gradient change threshold that triggers energy management can be dynamically adjusted.

[0143] In some embodiments, alternatively, instead of using a single threshold, multiple levels of warning and action thresholds are set. For example:

[0144] Level 1 (Prompt) Threshold: Gentle slope, only provides economic driving suggestions on the instrument panel;

[0145] Level 2 (Management) Threshold: Triggers predictive energy management (such as power limiting);

[0146] Level 3 (Safety) Threshold: Extremely steep slope. Based on energy management, safety constraint control (such as linkage retarder, limiting maximum vehicle speed) is forcibly triggered.

[0147] In some embodiments, the gradient change threshold can be dynamically adjusted based on the current battery charge and the vehicle's real-time load. When the battery charge is low, the trigger gradient threshold for uphill management is appropriately lowered to more actively protect the battery; when the battery charge is high, the trigger threshold for downhill management is lowered to more actively reserve recovery space. The greater the load, the greater the vehicle inertia, and the higher the demand for braking on long downhill slopes and power on long uphill slopes, so the threshold can be adjusted accordingly.

[0148] In some embodiments, optionally, such as Figure 5 As shown, the vehicle energy management method also includes:

[0149] Step S1160: Obtain the security alert threshold;

[0150] Step S1162: Determine the slope characteristic parameters based on the slope mileage information. When the slope characteristic parameters are greater than or equal to the safety warning threshold, determine at least one instrument warning location on the standard path.

[0151] Step S1164: When the real-time location of the vehicle matches the location indicated by the instrument panel, the corresponding safety prompt is triggered.

[0152] In this embodiment, the slope characteristic parameters (such as slope extremes, curve curvature, etc.) of the standard path are analyzed and judged by a preset safety warning threshold. When the parameter reaches or exceeds the safety warning threshold, the corresponding position is determined as the instrument warning position.

[0153] During vehicle operation, by continuously matching real-time location with preset warning locations, corresponding visual, auditory, or tactile safety prompts are automatically triggered before or upon arrival. This transforms static road hazard features into dynamic, location-based warning commands, providing timely and precise safety assistance to the driver and effectively improving driving safety on complex or dangerous road sections.

[0154] The cloud processing layer reads preset safety warning threshold parameters from the configuration database or rule engine. These safety warning threshold parameters are engineering-verified quantitative standards used to define dangerous road conditions that require warnings to drivers.

[0155] Safety warning thresholds are a set of or more judgment parameters that focus on driving safety risks.

[0156] For example, safety warning thresholds include, but are not limited to: slope threshold: for example, a slope with an absolute value exceeding 8% is considered a dangerous steep slope and requires a warning; curvature threshold: at a bend, if the radius of curvature of the path is less than a certain value (such as 30 meters), it is considered a sharp bend; composite feature threshold: such as "a continuous downhill length exceeding 2 kilometers and an average slope exceeding 5%" is considered a long steep downhill dangerous section.

[0157] Safety warning thresholds can be scientifically set based on mining area safety regulations, vehicle stability control parameters, and the driver's average reaction time.

[0158] The cloud processing layer scans the slope mileage information and extracts feature values ​​that characterize the degree of danger of local road sections, namely slope feature parameters. Slope feature parameters include: the slope value at the current point, the maximum slope over a distance ahead, the slope change rate (the second derivative of the slope, reflecting the rate of change of steepness), and a composite value combined with curve information.

[0159] The calculated slope characteristic parameters are compared with the safety warning threshold in real time.

[0160] Once the conditions are met, the cloud processing layer marks the precise latitude and longitude coordinates of the current point (or the starting point of the dangerous section) as an instrument alert location. The instrument alert location is a spatial signal point where danger is about to begin.

[0161] During vehicle operation, the vehicle terminal continuously compares its real-time location with the list of instrument prompt locations stored in the local energy management control commands. When the vehicle enters a preset range of an instrument prompt location (e.g., 50 meters ahead), a successful match is determined.

[0162] For example, commands are sent to the instrument cluster or infotainment system via an in-vehicle network (such as a CAN bus) to trigger preset prompt actions. These prompt actions include: visual prompts: displaying prominent icons (such as steep slope or curve signs) and text (such as "Slow down on steep slopes" or "Slow down on sharp curves") on the instrument panel; auditory prompts: emitting a "beep" warning sound or a voice announcement; and tactile prompts (such as configuration prompts): providing a prompt through seat vibration.

[0163] Understandably, traditional safety relies on the driver's instantaneous observation. This method, through cloud-based big data analysis, identifies all inherent hazards across the entire road network in advance and transforms them into actionable warning commands. This allows vehicles to systematically and comprehensively issue warnings before the driver even sees the danger, greatly expanding the driver's safety perception boundaries and reaction time.

[0164] Furthermore, the instrument prompt location and energy management location can be generated in parallel based on the same set of slope and mileage information, sharing a common location matching and triggering mechanism.

[0165] This means that a long, steep downhill section can be simultaneously marked as an energy management location (triggering strong energy recovery) and an instrument warning location (triggering a "slow down" warning), achieving perfect coordination between safety control and energy recovery, while improving both safety and economy.

[0166] In some embodiments, the safety prompts may optionally be configured with multiple warning systems: Level 1 prompt (informative): When the distance to the danger point is relatively far (e.g., 300 meters), a gentle reminder is given through an instrument icon or a soft sound; Level 2 prompt (warning): When approaching the danger point (e.g., 100 meters), the icon flashes, accompanied by a more urgent warning sound; Level 3 prompt (mandatory): When approaching the danger point (e.g., 50 meters), in addition to audible and visual prompts, the vehicle speed limit may be automatically limited, or the seat belt may be slightly pre-tensioned.

[0167] In some embodiments, the safety warning threshold can be dynamically adjusted based on factors such as the vehicle's real-time load, tire wear, and braking system temperature. For example, when the vehicle is heavily loaded, the braking distance is longer, and the downhill warning threshold should be increased accordingly (earlier warning).

[0168] In some embodiments, optionally, such as Figure 6 As shown, the vehicle energy management method also includes:

[0169] Step S1180: Obtain the front-end service from the cloud;

[0170] Step S1182: Send the energy management control command to the vehicle terminal;

[0171] Step S1184: The vehicle terminal receives the energy management control command issued and forwards it to the vehicle controller through the vehicle control bus;

[0172] Step S1186: The vehicle controller receives and stores the energy management control command;

[0173] Step S1188: When the real-time location of the vehicle matches the energy management location in the energy management control command, the vehicle controller executes the corresponding energy management control command or triggers the corresponding safety prompt.

[0174] In this embodiment, the generated energy management control commands are sent to the vehicle terminal via a cloud-based front-end service. After receiving the data, the vehicle terminal forwards it to the vehicle controller, which serves as the control core, via the onboard control bus. The vehicle controller is responsible for receiving and locally storing the energy management control commands.

[0175] During vehicle operation, the vehicle controller continuously compares the vehicle's real-time location with the energy management location in the energy management control command. Once a match is found, the corresponding energy management control command is automatically executed or a safety warning is triggered.

[0176] The vehicle terminal (or cloud-based dispatch system) needs to specify which particular software service module in the cloud it connects to for sending and receiving data. This typically involves calling a specific application programming interface (API), accessing a predefined service address, or joining a message queue.

[0177] The cloud-based front-end service refers to a software module deployed on a cloud server, specifically responsible for providing data interaction services to the outside world. The cloud-based front-end service is not a back-end computing and analysis program, but rather a service window facing the vehicle. In the vehicle energy management system, the core responsibility of the cloud-based front-end service is to receive requests for the generation of energy management and control commands, retrieve the latest data from the database (path information database), encapsulate it according to a predetermined protocol, and await its distribution. This is the starting point for data distribution.

[0178] The front-end service sends energy management control commands to the vehicle terminal of the target vehicle via the mobile communication network.

[0179] The energy management control command is the final output of the entire cloud processing workflow. It is a structured data packet. Its data structure can be encapsulated as a list, with each entry containing: {key point type, longitude, latitude, control command}.

[0180] The vehicle's terminal receives downlink data from the cloud via a communication module.

[0181] The vehicle terminal acts as an agent, repackaging the received energy management control commands according to the vehicle's internal communication protocol and sending them to the vehicle controller responsible for vehicle control via the onboard control bus.

[0182] The vehicle controller receives energy management control commands forwarded from the vehicle terminal via the CAN bus interface.

[0183] The vehicle controller writes the received complete energy management control commands into its internal non-volatile memory. This means that the data is not lost after the vehicle is turned off and can be directly retrieved upon the next power-on and unlocking without needing to be re-downloaded, ensuring the persistence and availability of the strategy.

[0184] The vehicle controller obtains real-time latitude and longitude data from the vehicle's GPS module via the in-vehicle network. The controller runs a matching algorithm that continuously compares the real-time location with all energy management control commands stored locally. A successful match is determined when the real-time location enters a pre-defined geofence around a key point.

[0185] Once the matching is successful, the vehicle controller, as the highest actuator, immediately executes the preset action bound to that key point:

[0186] If it is a critical point for energy management, then the energy management control commands are parsed and executed, such as sending specific torque, power, and gear limit values ​​to the motor controller, generator controller, and gearbox controller via the CAN bus.

[0187] If the instrument indicates a critical point, a specific display command is sent to the instrument cluster via the CAN bus to drive it to display a warning icon or text.

[0188] Understandably, by adopting a download-store-local matching and execution model, the system transforms its strong dependence on the real-time network into a weak dependence on the initial download. Once the policy data is successfully downloaded and stored in the vehicle controller, the vehicle can still autonomously and reliably execute all predictive energy management and safety measures during subsequent driving (even deep in mining areas with poor network signals). This greatly improves the system's practicality and robustness, adapting to the harsh communication environment of mines.

[0189] The vehicle controller can store multiple sets of energy management control commands, such as sunny economy mode strategy, rainy safety mode strategy, and heavy load enhancement strategy. The vehicle energy management system can automatically select and activate the most suitable strategy set based on real-time weather and load information.

[0190] When executing energy management control commands, the vehicle controller does not mechanically execute fixed parameters sent from the cloud, but rather fine-tunes the command parameters based on the vehicle's real-time status. For example, if the cloud command is "limit torque output to 70%", the vehicle can dynamically adjust the limit to 65% or 75% based on temperature and battery status to achieve better control.

[0191] After executing commands, the vehicle-mounted controller can record key execution result data (such as actual energy consumption on the slope and changes in battery state of charge) and report it to the cloud after the network is restored. The cloud can use this feedback data to evaluate the effectiveness of the strategy and optimize future slope change thresholds and control commands.

[0192] Furthermore, for the highest level of safety prompts or control commands (such as mandatory speed limits), a human-machine interaction confirmation process is included before execution. For example, a prompt may pop up on the instrument panel, requiring the driver to confirm within a certain time (e.g., 3 seconds). If no confirmation is received, the system will then execute the command automatically, balancing safety with the driver's ultimate control.

[0193] In some embodiments, optionally, executing corresponding energy management control commands to adjust the vehicle's power parameters includes: dynamically limiting the vehicle's drive motor torque, generator power, or transmission gear position according to the received energy management control commands.

[0194] In this embodiment, after receiving the energy management control command from the cloud, the vehicle controller implements dynamic and constrained control over one or more of the three core power parameters of the vehicle—drive motor torque, generator power, and gearbox gear position—through the vehicle control network, in accordance with road conditions and vehicle status.

[0195] Once the vehicle controller determines that an energy management control command needs to be executed through position matching, it immediately parses the command. This command contains a clearly defined control target and parameters. The vehicle controller then sends a command frame with specific parameter values ​​to the corresponding lower-level controller via the vehicle's internal CAN bus to achieve dynamic limiting.

[0196] Constraint control includes, but is not limited to, the vehicle controller sending a torque limit command to the drive motor controller. The motor controller will then adjust its internal algorithm accordingly to ensure that the actual torque output by the motor does not exceed the set upper limit.

[0197] It is mainly used for starting on long uphill or steep slopes. By limiting the peak torque in advance, it can prevent the battery from discharging at a high power instantaneously when the driver presses the accelerator hard at the beginning of the climb, thereby protecting the battery, smoothing the energy consumption curve, and avoiding slippage due to excessive torque.

[0198] The vehicle controller sends a power limit command to the generator controller (or a motor controller that integrates power generation function) to control the maximum power of its power generation (energy recovery).

[0199] Primarily used for long downhill sections. Before entering a downhill section, if the battery charge is already high, the power generation is limited to prevent overcharging. During a continuous downhill section, the power generation is dynamically adjusted to ensure a reasonable distribution with the mechanical braking force, maximizing energy recovery while ensuring braking safety and preventing the power generation system from overheating.

[0200] The vehicle controller sends instructions to the automatic transmission controller to limit the highest gear it can shift into. For example, it may restrict the transmission to shift only between 1st and 3rd gear, and prohibit upshifting into 4th gear.

[0201] Used for both uphill and downhill driving. When going uphill, limiting the highest gear ensures that the engine or motor operates within a high-efficiency, high-torque speed range, preventing insufficient power, frequent downshifting, or continuous high-load battery discharge caused by excessively high gears. When going downhill, using a lower gear (engine braking gear) enhances the slowing effect and reduces the load on the service brakes.

[0202] In one specific embodiment, optionally, the vehicle energy management method for a mining vehicle safety and energy management system includes:

[0203] Establish a cloud-vehicle collaborative system:

[0204] (1) Cloud layer:

[0205] The vehicle's Global Positioning System (GPS) / operational data is collected through the vehicle-mounted telematics terminal (T-BOX) → an adaptive parameter clustering algorithm is used to generate mining area road condition information (slope / altitude / mileage) → key control points are extracted.

[0206] (2) Cloud-to-vehicle communication:

[0207] T-BOX receives cloud data via 4G and then forwards it to the vehicle control unit (VCU) in packets.

[0208] (3) End level:

[0209] The VCU, based on real-time location and cloud-based road map (which transmits cloud-based road condition information to the vehicle via T-BOX), executes predictive energy management strategies at key points. At the same time, the instrument panel displays real-time safety alerts (such as steep slope warnings) at safety-critical points.

[0210] A schematic diagram of a mine vehicle safety and energy management system is shown below. Figure 10As shown, the cloud is the brain of the system, responsible for processing, analyzing, and making decisions on massive amounts of data:

[0211] Data cleaning is the first step in data processing, responsible for processing the raw GPS / operational data uploaded from the vehicle's T-BOX.

[0212] Data cleaning includes the following sub-modules:

[0213] Data noise removal: Filter out abnormal points such as GPS signal drift and jumps to ensure the accuracy of trajectory data.

[0214] Travel trajectory segmentation: Based on the vehicle operation cycle (such as from loading point to unloading point and back), the continuous data stream is segmented into meaningful independent travel trajectories to facilitate subsequent analysis.

[0215] Path generation is based on cleaned trajectory data, which is used to intelligently construct a standard digital map of the mining area.

[0216] Path generation includes the following submodules:

[0217] Clustering Algorithm Analysis: Core Algorithm. This algorithm intelligently analyzes the trajectories of multiple vehicles traveling multiple times, merges trajectories with the same path, distinguishes paths leading to different destinations, and thus automatically learns and extracts all standard driving routes within the mining area.

[0218] Path smoothing: The paths generated by clustering are smoothed and optimized to eliminate local jitter and form continuous and smooth standard paths.

[0219] Path information analysis involves in-depth mining of the generated standard path to extract key parameters that directly affect vehicle control and safety.

[0220] Outputs of path information analysis:

[0221] Slope-mileage information - altitude-mileage: Calculate the slope value and altitude of each point on the path to form a complete slope-mileage curve and altitude-mileage curve, which is the basis for all subsequent strategy formulation.

[0222] Uphill / downhill points: Based on slope information, identify key energy management points along the path where the slope changes significantly (such as the starting point of a long uphill / downhill slope).

[0223] Instrument panel highlights key points: Based on slope or road conditions, identify key safety points that require warning to the driver (such as steep slopes and sharp bends).

[0224] The data is stored through a driving database and a route information database. The driving database stores the original and cleaned vehicle driving trajectory data; the route information database stores the final standardized route and all its information such as slope, altitude, and key points, i.e., cloud-based road map or digital road book.

[0225] The front-end service serves as the external service window and control panel for the cloud. Its main functions include:

[0226] Route information display: Provides a visual interface for back-end administrators or monitoring systems to display information such as the mining area road network, vehicle location, and key points.

[0227] Key information distribution: The route information database and key point strategies are packaged and sent to the vehicle through the platform data interface. This is the output outlet for cloud control commands.

[0228] The platform data interface is a dedicated communication bridge and data exchange standard between the cloud and the vehicle.

[0229] The platform's data interface enables bidirectional data transmission: Vehicle information upload: receiving real-time vehicle status, location, and other data uploaded from the vehicle's T-BOX; Route information distribution: reliably distributing cloud-processed energy management and control command packages to the vehicle's T-BOX.

[0230] The vehicle-mounted terminal (T-BOX) is responsible for receiving and accurately executing instructions from the cloud. It maintains a connection with the cloud via a wireless network and platform data interface. It receives route information from the cloud and uploads vehicle-mounted information such as GPS, speed, and battery status.

[0231] The cloud commands received are forwarded to the VCU via the CAN bus, and the vehicle-side data collected by the VCU is uploaded at the same time.

[0232] The Vehicle Control Unit (VCU) is the ultimate executor of cloud-based strategies. It receives and stores energy management control commands from the cloud via the CAN bus from the T-BOX. It executes specific control strategies, continuously matching the vehicle's real-time location with the energy management location data sent from the cloud.

[0233] When matching is successful: Preset actions are executed automatically. For example, torque is adjusted and power generation is limited at key energy management points; warning information is displayed on the instrument panel at key instrument prompt points.

[0234] Specifically, cloud-based data processing includes:

[0235] Data cleaning: Remove GPS noise and correct trajectory deviations;

[0236] Path analysis: By using an adaptive parameter clustering algorithm to fuse similar trajectories, distinguish different trajectories, and extract the vehicle's driving path from the trajectories;

[0237] Key point extraction: Automatically analyze two types of key points in the path: energy management key points and safety warning key points (both types of key points are implemented through slope segmentation strategies and algorithms. Points where slope changes can affect the vehicle's energy management strategy can be considered as our energy management points; points where slope changes require safety warnings to the driver are the key points that need safety warnings).

[0238] Cloud-vehicle communication includes: T-BOX receiving cloud data via 4G technology → packetizing and forwarding to VCU; packaging {longitude, latitude, altitude, slope, continuous mileage, energy management control information, vehicle safety control information} into a set of metadata; sending the metadata to T-BOX and VCU in the order of loading point, unloading point, loading point, and so on, and storing it locally on the controller.

[0239] Vehicle-side control strategies include:

[0240] Predictive energy management: The metadata received by the VCU includes (longitude, latitude, altitude). It matches the vehicle's real-time location with the location in the metadata sent from the cloud. If a match is found, it performs energy management based on the energy management control information in the metadata and provides instrument prompts based on the vehicle safety control information.

[0241] The specific energy management and control information includes: generator line restrictions, torque restrictions, highest gear restrictions, and generator shutdown restrictions.

[0242] The energy management control information is defined differently for different vehicles.

[0243] like Figure 7As shown in the figure, this application embodiment also provides a vehicle energy management device 900, including: a data acquisition module 902, used to acquire raw driving data of a target area; a data processing module 904, used to perform trajectory cleaning and trajectory segmentation processing on the raw driving data to determine at least one trajectory segment; a path generation module 906, used to generate a standard path based on at least one trajectory segment; an information extraction module 908, used to perform path information analysis on the standard path to extract slope mileage information; a threshold determination module 910, used to acquire a slope change threshold; a feature determination module 912, used to determine a slope change parameter based on the slope mileage information, and when the slope change parameter is greater than or equal to the slope change threshold, determine at least one energy management position on the standard path; a position acquisition module 914, used to acquire the real-time position of the vehicle; and an energy management module 916, used to control the vehicle terminal to execute the corresponding energy management control command to adjust the vehicle's power parameters when the real-time position matches the energy management position.

[0244] like Figure 8 As shown in the figure, this application embodiment also provides a vehicle energy management system 100, which includes a cloud processing layer 102 and at least one vehicle terminal 104. The at least one vehicle terminal 104 is communicatively connected to the cloud processing layer 102 to receive energy management control commands issued by the cloud processing layer 102.

[0245] like Figure 9 As shown, this application embodiment also provides a vehicle 200, which is equipped with a vehicle terminal 104 in a vehicle energy management system 100.

[0246] For example, the vehicles 200 on which the vehicle terminal 104 is installed include, but are not limited to, mining vehicles.

[0247] In this invention, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance; the term "multiple" refers to two or more unless otherwise explicitly defined. The terms "install," "connect," "link," and "fix" should be interpreted broadly. For example, "connect" can be a fixed connection, a detachable connection, or an integral connection; "link" can be a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0248] In the description of this invention, it should be understood that the terms "upper," "lower," "left," "right," "front," "rear," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0249] In the description of this specification, the terms "one embodiment," "some embodiments," "specific embodiment," etc., refer to specific features, structures, materials, or characteristics described in connection with an embodiment or example that are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0250] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle energy management method, characterized in that, A vehicle energy management system is used, the vehicle energy management system including a cloud processing layer and at least one vehicle terminal, the at least one vehicle terminal being communicatively connected to the cloud processing layer to receive energy management control commands issued by the cloud processing layer, the vehicle energy management method being used in the cloud processing layer, the vehicle energy management method including: Obtain raw driving data for the target area; The original driving data is subjected to trajectory cleaning and trajectory segmentation to determine at least one trajectory segment; Generate a standard path based on at least one of the trajectory segments; The standard route is analyzed to extract slope and mileage information. Obtain the slope change threshold; Based on the slope mileage information, a slope change parameter is determined. When the slope change parameter is greater than or equal to the slope change threshold, at least one energy management location on the standard path is determined. Obtain the real-time location of the vehicle; When the real-time location matches the energy management location, the vehicle terminal is controlled to execute the corresponding energy management control command to adjust the vehicle's power parameters.

2. The vehicle energy management method according to claim 1, characterized in that, The process of cleaning and segmenting the original driving data to determine at least one trajectory segment includes: The original driving data is subjected to noise removal to determine the purified trajectory data; Obtain the location information parameters of the loading point and unloading point; The purification trajectory data is segmented according to the location information parameters to obtain at least one independent round-trip trajectory segment as the processed trajectory segment.

3. The vehicle energy management method according to claim 1, characterized in that, The step of generating a standard path based on at least one of the trajectory segments includes: An adaptive parameter clustering algorithm is used to analyze multiple trajectory segments to distinguish different driving paths and merge trajectory segments with the same driving path, thereby extracting the standard driving path. The standard driving path is smoothed to generate the standard path containing a continuous sequence of latitude and longitude.

4. The vehicle energy management method according to claim 3, characterized in that, The step of determining the slope change parameter based on the slope mileage information, and determining at least one energy management location on the standard path when the slope change parameter is greater than or equal to the slope change threshold, includes: Based on the slope mileage information, determine the slope and slope change trend of the trajectory segment; When the slope change trend indicates a continuous uphill or downhill section, and the absolute value of the slope or the slope change rate of the trajectory segment is greater than or equal to the slope change threshold, the starting point of the trajectory segment is determined as the energy management location.

5. The vehicle energy management method according to claim 1, characterized in that, Also includes: Obtain the security alert threshold; Based on the slope mileage information, a slope characteristic parameter is determined. When the slope characteristic parameter is greater than or equal to the safety warning threshold, at least one instrument warning location on the standard path is determined. When the real-time location of the vehicle matches the location indicated by the instrument panel, a corresponding safety warning is triggered.

6. The vehicle energy management method according to claim 1, characterized in that, Also includes: Obtain front-end services from the cloud; The energy management control command is sent to the vehicle terminal; The vehicle terminal receives the energy management control command issued and forwards it to the vehicle controller via the vehicle control bus; The vehicle controller receives and stores the energy management control command; When the real-time location of the vehicle matches the energy management location in the energy management control command, the vehicle controller executes the corresponding energy management control command or triggers the corresponding safety prompt.

7. The vehicle energy management method according to any one of claims 1 to 6, characterized in that, The execution of the corresponding energy management control command to adjust the vehicle's power parameters includes: Based on the received energy management control command, the torque of the vehicle's drive motor, the power of the generator, or the gearbox gear position are dynamically limited.

8. A vehicle energy management device, characterized in that, include: The data acquisition module is used to acquire raw driving data for the target area; The data processing module is used to perform trajectory cleaning and trajectory segmentation on the raw driving data to determine at least one trajectory segment. A path generation module is used to generate a standard path based on at least one of the trajectory segments; The information extraction module is used to analyze the path information of the standard path and extract slope and mileage information; The threshold determination module is used to obtain the slope change threshold; The feature determination module is used to determine the slope change parameter based on the slope mileage information, and when the slope change parameter is greater than or equal to the slope change threshold, determine at least one energy management location on the standard path; The location acquisition module is used to obtain the real-time location of the vehicle; The energy management module is used to control the vehicle terminal to execute corresponding energy management control commands to adjust the vehicle's power parameters when the real-time location matches the energy management location.

9. A vehicle energy management system, characterized in that, The vehicle energy management system includes a cloud processing layer and at least one vehicle terminal. The at least one vehicle terminal is communicatively connected to the cloud processing layer to receive energy management control commands issued by the cloud processing layer.

10. A vehicle, characterized in that, The vehicle terminal is installed in the vehicle energy management system as described in claim 9.

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