Vehicle control method and vehicle

Abstract

The invention discloses a vehicle control method which is applied to a vehicle provided with a vehicle-mounted power-assisted bicycle, the power-assisted bicycle is detachably arranged in a closed storage bin of the vehicle, and the vehicle control method comprises the steps that a storing and taking operation instruction for the power-assisted bicycle or a storing and taking request autonomously generated by a system is obtained; in response to the access operation instruction or the access request, verifying whether the vehicle is in a safe state allowing access; based on the multi-source data, the potential use requirement of the user for the power-assisted bicycle is judged, and at least one preprocessing operation is executed according to the judgment result; if the vehicle state verification is passed, controlling a vehicle-mounted automatic storage and taking mechanism to execute a power-assisted bicycle storage and taking action corresponding to the storage and taking operation instruction or the storage and taking request; and when the power-assisted bicycle is in the vehicle-mounted storage state, the charging management strategy or the physical maintenance strategy of the power-assisted bicycle is adaptively adjusted according to the dynamic condition. The invention further provides the vehicle capable of achieving the vehicle control method.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a vehicle control method and a vehicle. Background Technology

[0002] With the diversification of urban commuting and leisure travel modes, the combination of car and e-bike travel is becoming increasingly popular. After driving to scenic spots, business districts, or the outskirts of residential areas, users can switch to e-bikes to complete the last mile, effectively avoiding congestion and solving parking problems.

[0003] Currently, users mainly rely on external accessories, such as roof racks or rear-mounted bicycle racks, to carry e-bikes. These methods secure the e-bikes outside the main vehicle structure for transport. However, the storage and retrieval process of existing solutions requires manual handling and securing by the user, which is time-consuming and labor-intensive. During operation, the externally mounted e-bike and its support are susceptible to wind resistance, generating additional noise and energy consumption, and there is a risk of loosening and detachment. Furthermore, the data between functional modules in existing solutions is not shared, and the control logic is isolated, resulting in mechanically superimposed system functions rather than organically integrated ones. Therefore, existing onboard e-bike solutions do not constitute an efficient and collaborative intelligent integrated system, and there is significant room for improvement in terms of user experience intelligence, ease of operation, and operational safety. Summary of the Invention

[0004] This application provides a vehicle control method aimed at solving the problem that vehicle-mounted electric bicycles are independent of each other and lack interaction in multiple aspects such as access control and state prediction.

[0005] To achieve the above technical objectives, this application provides the following technical solution: In a first aspect, one embodiment of this application provides a vehicle control method applied to a vehicle equipped with a vehicle-mounted electric bicycle, the electric bicycle being detachably disposed in a closed storage compartment of the vehicle, the method comprising: Obtain access commands or system-generated access requests for the electric bicycle; In response to the access operation command or the access request, verify whether the vehicle is in a safe state that allows access; determine the user's potential use demand for the electric bicycle based on multi-source data, and perform at least one preprocessing operation according to the determination result; If the vehicle status verification is successful, the on-board automatic access mechanism is controlled to perform the assistive bicycle access action corresponding to the access operation command or access request. When the electric bicycle is in the vehicle-mounted storage state, the charging management strategy or physical retention strategy for the electric bicycle is adaptively adjusted according to dynamic conditions.

[0006] In conjunction with the first aspect, in some embodiments of the first aspect, the step of determining a user's potential demand for the e-bike based on multi-source data and performing at least one preprocessing operation based on the determination result specifically includes: Establish and continuously update personalized travel habit profiles for users, which are generated based on users' historical travel data and associated with travel time, destination location, and whether or not the user uses the e-bike. Obtain real-time dynamic information related to the current trip, including the vehicle's navigation destination semantic information, real-time traffic flow data, parking resource information around the destination, and weather information; The real-time dynamic information is matched and calculated with the travel habit profile to generate a demand prediction confidence level; Specifically, the preprocessing operation is triggered only when the confidence level of the demand prediction exceeds the dynamically adjusted activation threshold; the preprocessing operation includes at least one of adjusting the internal environmental parameters of the closed storage compartment and preheating or precooling the battery of the electric bicycle.

[0007] In conjunction with the first aspect, in some embodiments of the first aspect, the operation corresponding to the physical holding strategy is to dynamically adjust the clamping force of the locking mechanism in the closed storage compartment on the electric bicycle according to the bumpiness of the road surface on which the vehicle is traveling, or to adjust the damping coefficient of the shock-absorbing element in the closed storage compartment. The operation corresponding to the charging management strategy is to control the electric bicycle battery to discharge to the vehicle's low-voltage electrical system when it is detected that the vehicle is in a parked state and its low-voltage battery power is lower than a first set value, while the electric bicycle battery power is higher than a second set value.

[0008] In conjunction with the first aspect, in some embodiments of the first aspect, during the process of controlling the on-board automatic access mechanism to execute the electric bicycle access action corresponding to the access operation command or the access request, the following steps are performed simultaneously: By fusing data from at least two different types of sensors located around the access port, a dynamic environment model outside the access port is constructed in real time. Identify potential risk elements in the dynamic environment model, including moving objects and low, stationary obstacles; Risk assessment is performed based on the type of identified risk factors, their trajectory, and the current motion state of the automated access mechanism. Based on different risk assessment levels, corresponding graded safety responses are executed, including: reducing movement speed and enhancing warnings, or immediately suspending movement and activating audible and visual alarms.

[0009] In conjunction with the first aspect, in some embodiments of the first aspect, performing the electric bicycle storage action includes: Control the opening of the closed storage compartment door and move the support part of the automatic storage mechanism to the external preparatory position; After the electric bicycle is placed in the support unit and locked, the support unit is retracted into the closed storage compartment and the compartment door is closed. After the supporting part is retracted into place, the charging interface located in the enclosed storage compartment is automatically connected to the charging interface of the electric bicycle, and charging is started.

[0010] In conjunction with the first aspect, in some embodiments of the first aspect, the method further includes: Monitor and record the storage status, battery health status, and data from each access and charge / discharge cycle of the electric bicycle. Based on the time series of the recorded access and charge / discharge data, the battery degradation trend is analyzed, and combined with the storage status, a maintenance suggestion report containing maintenance reminders or fault warnings is generated and pushed to the user terminal.

[0011] In conjunction with the first aspect, in some embodiments of the first aspect, the control of the on-board automatic access mechanism to perform the assistive bicycle access action corresponding to the access operation command or the access request further includes: Real-time acquisition and fusion of environmental perception data, vehicle status data, and command priority information to form a comprehensive decision dataset; Based on the comprehensive decision dataset, the optimal motion control sequence of the automatic access mechanism is dynamically generated. The optimal motion control sequence includes the planning of the movement path and speed curve and its coordination with the execution timing of the preprocessing operation. Based on the optimal motion control sequence, the automatic access mechanism is controlled to perform access actions.

[0012] In conjunction with the first aspect, in some embodiments of the first aspect, the operation corresponding to the physical retention strategy further includes: Continuously monitor the vehicle's dynamic driving parameters and predict potential severe impact risks based on parameter change trends; When the predicted risk exceeds a preset threshold, before the actual impact occurs, the locking mechanism of the closed storage compartment is actively controlled to enhance its force, and / or the electrical connection path between the electric bicycle battery and the vehicle charging system is cut off.

[0013] In conjunction with the first aspect, in some embodiments of the first aspect, when the electric bicycle is in an onboard storage state, the cooperative charging strategy for the electric bicycle is adaptively adjusted according to dynamic conditions, and the operation corresponding to the cooperative charging strategy is as follows: The battery of the electric bicycle is used as a controllable energy storage node in the vehicle's integrated energy management network. Based on the overall operating conditions of the vehicle, external power grid signals, user-preset scenario modes, and the priority of each power-consuming unit, the system collaboratively decides the charging and discharging timing and power of the electric bicycle battery in different scenarios such as vehicle driving, parking, or external power supply through a predetermined energy allocation strategy model.

[0014] Secondly, one embodiment of this application provides an integrated system for a vehicle-mounted electric bicycle, used to implement the cooperative control method described above, the system comprising: The enclosed storage compartment is located inside the vehicle body. An automatic storage and retrieval mechanism is installed inside the storage compartment; A vehicle domain controller is installed in the vehicle; The central control unit is communicatively connected to the vehicle domain controller and the automatic access mechanism. The central control unit is configured as follows: Obtain access commands or system-generated access requests for the electric bicycle; In response to the access operation command or the access request, verify whether the vehicle is in a safe state that allows access; determine the user's potential use demand for the electric bicycle based on multi-source data, and perform at least one preprocessing operation according to the determination result; If the vehicle status verification is successful, the on-board automatic access mechanism is controlled to perform the assistive bicycle access action corresponding to the access operation command or access request. When the electric bicycle is in the vehicle-mounted storage state, the charging management strategy or physical retention strategy for the electric bicycle is adaptively adjusted according to dynamic conditions.

[0015] Thirdly, one embodiment of this application also provides a vehicle, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle control method described above.

[0016] Fourthly, one embodiment of this application also provides a computer program product, the computer program product including a computer program, which, when run by a processor, implements the vehicle control method as described above.

[0017] Fifthly, one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle control method described above.

[0018] As can be seen from the above technical solutions, the vehicle-mounted electric bicycle integrated system, vehicle, computer program product, and computer-readable storage medium provided in this application all correspondingly implement the vehicle control method described in the first aspect. This method uses access operation commands or autonomous system requests for the electric bicycle as unified trigger events. During response, it simultaneously executes potential usage demand judgment and preprocessing based on multi-source data, and verifies the vehicle's safety status, thereby achieving a shift from passive response to proactive service and preparation. After safety verification, it controls the automatic access mechanism to perform access actions, ensuring safe and reliable operation. During the electric bicycle's storage period, it adaptively adjusts its physical holding strategy and charging management strategy according to dynamic conditions, enabling the bicycle battery to participate in energy sharing as an intelligent node in the vehicle's energy network, achieving optimization of energy utilization efficiency and functional reuse. This method integrates discrete access control, predictive maintenance, and dynamic energy management through a closed-loop collaborative logic of intelligent response and preprocessing-safe execution-continuous adaptive optimization, merging the electric bicycle and vehicle systems at the data and control levels to form a highly efficient and collaborative intelligent integrated system. Attached Figure Description

[0019] To more clearly illustrate the technical solution of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A flowchart of a vehicle control method provided for one embodiment of this application.

[0021] Figure 2 A structural block diagram of an integrated system for a vehicle-mounted power-assisted bicycle provided for one embodiment of this application.

[0022] Figure 3 This is a schematic diagram of the architecture of a vehicle provided for one embodiment of this application. Detailed Implementation

[0023] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are merely used to avoid confusion of the constituent elements.

[0024] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this application. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] SUMMARY Existing solutions for integrating bicycles and cars mostly focus on solving the integration problem in physical space, exhibiting significant modular and isolated characteristics in terms of functional implementation. This isolation leads to a series of system-level problems. First, the functional response is delayed and passive: the system only begins to act after receiving a clear user access command, unable to proactively prepare based on trip information and user habits. Second, state management is rudimentary, unable to dynamically optimize based on real-time road conditions and vehicle energy status. Third, safety monitoring during the access process lacks limit switches, and there is a lack of proactive perception and intelligent response capabilities to dynamically changing environments.

[0027] Based on the analysis of the above situation, this application focuses on the problem of how to break down the functional barriers between multiple aspects of vehicle-mounted electric bicycles, such as access control, state prediction and dynamic energy management, and realize the deep integration and linkage of various functional modules at the data and control level, thereby forming a highly efficient and collaborative intelligent integrated system.

[0028] To address this issue, the starting point of this application is the recognition that an intelligent integrated system focuses on establishing a mechanism that enables sub-functional modules to perceive, influence, and make collaborative decisions. Therefore, the concept of this application is to establish a collaborative control framework centered on a central control unit. This framework defines the access collaborative control process at the physical execution layer and the intelligent management process at the intelligent decision-making layer as two equal and continuously interacting core processes.

[0029] The access control process, acting as the executor, is responsible for safely and accurately completing all operational sequences related to the physical movement of the e-bike, mechanical locking, and electrical interface connection / disconnection. It strictly adheres to safety procedures and provides real-time feedback on its execution status. The intelligent management process, as the system's central hub, includes demand forecasting and preprocessing for future needs and adaptive management during operation for real-time state optimization. The data interaction and command coordination channels established between these two processes are crucial for achieving deep integration.

[0030] Data interaction means that the intelligent management process needs the real-time status of the access process as the environmental input for its decision-making, and the access process also needs the output of the intelligent management process to optimize its own control parameters.

[0031] Command coordination means that the commands generated by the intelligent management process and the access control commands triggered by users or events are not simply executed in a queue. Instead, the central control unit performs unified timing planning and resource scheduling according to preset optimization strategies, which may be executed in parallel or interleaved to achieve optimal overall efficiency.

[0032] Through this deeply collaborative architecture, the system has transformed from passively responding to single commands to proactively providing coherent services, and from optimizing local functions to achieving global system optimization. Users gain a seamless, smooth, secure, and intelligent last-mile connectivity experience, while the system itself achieves systematic improvements in operational safety, energy economy, component durability, and user experience satisfaction. The technical solution of this application will be described in detail below with reference to the accompanying drawings and embodiments.

[0033] Exemplary method One embodiment of this application provides a vehicle control method applied to a vehicle equipped with a vehicle-mounted electric bicycle, the electric bicycle being detachably mounted in a closed storage compartment of the vehicle. The vehicle control method includes: S101: Obtain access operation instructions or system-generated access requests for the electric bicycle.

[0034] In this embodiment, the access commands for the vehicle-mounted electric bicycle originate from user-initiated operations, which may include the following interaction channels: The user interface integrated into the vehicle infotainment system allows users to click virtual buttons to store or retrieve the vehicle. These commands are then transmitted to the central control unit via the vehicle's high-speed communication bus.

[0035] Users operate the system via a mobile app linked to the vehicle. Commands can be sent remotely to the vehicle's network control unit via cellular network and then forwarded to the central control unit; or, within the vehicle's Bluetooth range, directly via Bluetooth Low Energy protocol.

[0036] Physical buttons or knobs located near the driver's side door panel, center console, or tailgate are suitable for scenarios where the vehicle's infotainment system malfunctions or where the user prefers physical buttons. Button signals are connected to the central control unit via hardwired wiring or a LIN bus.

[0037] The smart key is activated by pressing a specific function key. The signal is received and decoded by a radio frequency receiver before being sent to the central control unit. Upon receiving the command, the central control unit first parses it to determine the command type and source.

[0038] In this embodiment, the access requests generated autonomously by the system are not directly triggered by the user, but are proactively generated by the system's internal intelligent management process after background analysis and calculation. Essentially, they are preparatory requests based on predictions of future events, made in advance to optimize the user experience. These autonomously generated requests are preparatory; they typically do not immediately trigger a complete physical access action, but they do trigger a series of background preprocessing operations and adjust the system state to high readiness. If the user subsequently issues a formal vehicle retrieval command, the system's response speed will be significantly improved due to the completed preprocessing. The priority of autonomous requests is usually lower than immediate user commands; when a user command arrives, the autonomous request operation can be paused or seamlessly integrated into the user command's execution flow.

[0039] S102: In response to an access operation command or access request, verify whether the vehicle is in a safe state that allows access; determine the user's potential demand for the e-bike based on multi-source data, and perform preprocessing operations according to the determination result.

[0040] Upon responding to an operation command or access request, two key tasks are executed in parallel: rigid verification to ensure operational safety, and intelligent prediction and preprocessing to improve the user experience.

[0041] 1. Vehicle safety status verification Before performing any mechanical action, the system must complete a multi-dimensional safety status verification to rule out the possibility of operation under hazardous conditions. The central control unit acquires and verifies the following key status parameters through real-time communication with the vehicle domain controller; all conditions must be met simultaneously for verification to pass: Confirm that the gearbox gear signal is park (P), and confirm the vehicle speed is 0 km / h through both wheel speed sensors and the vehicle controller to ensure the vehicle is completely stationary.

[0042] For electric vehicles, it is necessary to confirm that the vehicle's high-voltage system has been safely de-energized or is in a safe mode that allows for the operation of low-voltage accessories.

[0043] The active safety systems, such as the electronic stabilization program, were confirmed to have no abnormal alarms. At the same time, the central control unit performed a rapid power-on self-test on the sensors of the automatic storage and retrieval mechanism to confirm that they were fault-free.

[0044] Based on the integrated location of the enclosed storage compartment, confirm that the corresponding door or tailgate is unlocked and can be opened and closed freely.

[0045] The central control unit periodically monitors the above status. Upon receiving an access request, it immediately initiates an instant verification. If any condition is not met, the process is immediately interrupted, and a clear prompt is issued to the user through the human-machine interface. The process can only continue if all conditions are met and the verification passes.

[0046] 2. Potential demand assessment and preprocessing based on multi-source data Simultaneously or after successful security verification, the system performs intelligent demand forecasting and preprocessing in parallel. This function runs as an independent service thread within the central control unit, and its process can be divided into three stages: data perception, intelligent decision-making, and collaborative execution.

[0047] The system uses a dedicated data adapter to collect and standardize three types of information flows in real time: User behavior streams – capturing trip start and end events from the vehicle bus and querying historical e-bike usage records associated with the trip from a local encrypted database to build user behavior samples for analysis.

[0048] Real-time environmental streams—subscribe to semantic information of the navigation destination via the in-vehicle network; obtain parking resource information and real-time traffic flow data around the destination from cloud services through the vehicle-to-everything (V2X) control unit, and simultaneously obtain current weather information.

[0049] Vehicle Status Stream – Continuously monitors real-time status messages such as vehicle power mode, gear position, and battery level.

[0050] The user-personalized prediction model is trained periodically in the cloud based on the user's historical travel data. On the vehicle side, when key trip information is determined, the system extracts the current feature vector and inputs it into the model to obtain a basic usage probability. Subsequently, the system introduces a set of dynamic calibration factors, such as data freshness, weather impact, and trip certainty, to weight the basic usage probability and finally generate a demand prediction confidence level ranging from 0% to 100%.

[0051] The system maintains a dynamically adjusted activation threshold for each user. This threshold is adaptively optimized within a small range based on recent prediction accuracy to balance service proactivity and accuracy. Only when the calculated confidence level exceeds the current threshold is the system considered a high-confidence request and preprocessing is triggered.

[0052] Once preprocessing is triggered, the system will calculate the optimal preprocessing target based on factors such as the predicted vehicle retrieval time and ambient temperature. Subsequently, the central control unit sends commands to the relevant actuators, specifically including: Turn on the PTC heater or ventilation fan in the storage compartment, and use a closed-loop control algorithm to adjust and maintain the temperature in the compartment within a suitable range.

[0053] The battery core temperature of the electric bicycle is obtained through a communication link. If the temperature is lower than the optimal operating limit, the battery preheating program is activated to improve battery activity and discharge performance.

[0054] When vehicles are safe and idle, if strong demand is anticipated, the system can generate a low-priority background task to control the automated storage and retrieval mechanism to move the bicycle from its fully stored position to a ready position closer to the storage entrance. When the user issues a retrieval command, the storage and retrieval mechanism can reduce its response time.

[0055] S103: If the vehicle status verification is successful, control the on-board automatic storage and retrieval mechanism to perform the corresponding electric bicycle storage and retrieval action.

[0056] This step is executed only after the vehicle safety status verification in step S102 has passed. The central control unit sends a precise sequence of control commands to the on-board automatic storage and retrieval mechanism, specifying the action type, target location, and trajectory. Action types include storage and retrieval.

[0057] S104: When the e-bike is in the onboard storage state, the charging management strategy or physical holding strategy is adaptively adjusted according to dynamic conditions.

[0058] Once the e-bike has been stored and is in its onboard storage state, the system's management focus shifts to continuous adaptive optimization. This function is implemented by a series of background management threads within the central control unit, primarily encompassing two dimensions: The adaptive adjustment of the physical holding strategy involves the system using the vehicle's inertial measurement unit and suspension sensors to perceive the bumpiness of the road surface in real time. When entering an unpaved road surface or a continuous speed bump area, the system automatically instructs the locking mechanism inside the storage compartment to increase the clamping force on the vehicle and may simultaneously adjust the damping of the active shock absorbers to dynamically respond to impacts and enhance the protection level. After the vehicle enters a smooth road section, the parameters automatically recover, achieving flexible adaptive protection.

[0059] The system adaptively adjusts its charging management strategy, specifically managing the booster battery as an intelligent node in the vehicle's integrated energy network. After a booster is parked, the charging interface automatically connects, and the system can initiate intelligent charging based on settings or battery status. When the vehicle is parked, if the system detects that the vehicle's low-voltage battery is below a safe threshold while the booster battery is sufficiently charged, it can establish a discharge circuit to replenish the vehicle's battery with the booster battery, preventing depletion. Furthermore, the system can intelligently determine the charging and discharging timing and power of the booster battery based on the vehicle's overall operating conditions, external grid signals, or user-preset scenarios, using a predetermined energy allocation strategy model to optimize energy efficiency. In summary, this embodiment treats access commands or autonomous system requests for the electric bicycle as unified trigger events. During the response, it simultaneously performs potential usage demand assessment and preprocessing based on multi-source data, and verifies the vehicle's safety status, thus achieving a shift from passive response to proactive service and preparation. After successful safety verification, it controls the automatic access mechanism to perform access actions, ensuring safe and reliable operation. During the electric bicycle's storage, it adaptively adjusts its physical holding strategy and charging management strategy according to dynamic conditions, enabling the bicycle battery to participate in energy sharing as an intelligent node in the vehicle's energy network, optimizing energy utilization efficiency and achieving functional reuse. This method integrates discrete access control, predictive maintenance, and dynamic energy management through a closed-loop collaborative logic of intelligent response and preprocessing – safe execution – continuous adaptive optimization, merging the electric bicycle and vehicle systems at the data and control levels to form a highly efficient and collaborative intelligent integrated system.

[0060] In some optional embodiments, the step of determining the user's potential usage demand for the e-bike based on multi-source data and performing at least one preprocessing operation based on the determination result specifically includes: Establish and continuously update personalized travel habit profiles for users, which are generated based on users' historical travel data and associated with travel time, destination location, and whether or not the user uses the e-bike. Obtain real-time dynamic information related to the current trip, including the vehicle's navigation destination semantic information, real-time traffic flow data, parking resource information around the destination, and weather information; The real-time dynamic information is matched and calculated with the travel habit profile to generate a demand prediction confidence level; Specifically, the preprocessing operation is triggered only when the confidence level of the demand prediction exceeds the dynamically adjusted activation threshold; the preprocessing operation includes at least one of adjusting the internal environmental parameters of the closed storage compartment and preheating or precooling the battery of the electric bicycle.

[0061] The detailed construction of a user's personalized travel habit profile is as follows: The system anonymously collects data through the Internet of Vehicles. At the start of each trip, it records the timestamp and the GPS coordinates of the starting point. At the end of the trip, it records the GPS coordinates of the destination and the point of interest (POI).

[0062] Each successful e-bike retrieval operation is precisely recorded, including the operation time, the vehicle's GPS location at the time of the operation, and the operation type. By performing temporal and spatial correlation analysis between the e-bike retrieval event and the trip completion event, the system can determine whether an e-bike was used to complete the final segment of the trip.

[0063] When the system pops up suggestions based on predictions, the user's actions of clicking "accept," "ignore," or "cancel" are recorded as monitoring signals.

[0064] With user authorization and in compliance with privacy regulations, data is securely processed on the vehicle or in the cloud. A lightweight machine learning model, such as a gradient boosting decision tree, is employed. The model's input features are designed to include temporal, spatial, trip, and historical behavioral characteristics. The model's output is a probability value between 0 and 1, representing the likelihood of a user using the e-bike given that set of features. The model is trained offline and inferred online. As new data accumulates, incremental learning algorithms are used to fine-tune and update the model on the cloud or vehicle, adapting it to gradual changes in user habits.

[0065] The acquisition and fusion of real-time dynamic information is specifically executed as follows: Deep integration with in-vehicle navigation systems allows for the acquisition of semantic information about navigation destinations. This involves not only obtaining the destination's latitude and longitude, but more importantly, parsing semantic tags and attributes of the destination through the navigation system's application programming interface (API) or high-precision map data. Semantic information is more accurate than coordinates in matching abstract categories within a user profile.

[0066] The system obtains real-time traffic flow data from a cloud-based traffic information service platform through the vehicle-to-everything (V2X) control unit. It not only focuses on congestion along the entire route but also emphasizes analyzing the speed and congestion color of the last 500 meters to 1 kilometer. This directly reflects the micro-circulation of traffic in the destination area and the likelihood of parking queues.

[0067] The system accesses the smart city parking management platform's application programming interface (API) to obtain parking resource information around the destination. This data can include the real-time total number of available parking spaces in all public parking lots within a 500-meter radius of the destination, or a parking convenience index (a score from 0-100) predicted based on historical big data for the current time period. For areas without real-time data, the system can estimate parking availability using historical average data based on POI type and time. It also obtains current and 1-2 hour weather forecasts from vehicle-to-everything (V2X) weather services, including temperature, perceived temperature, probability of precipitation, wind speed, and UV intensity. This information directly impacts users' willingness to ride and the performance of the e-bike's battery.

[0068] During each trip, the prediction module initiates a calculation once the navigation destination is set or changed. Based on the current real-time dynamic information, it constructs a feature vector F for the current scene. current F current Input the pre-trained user profile model to obtain the base probability P. base Considering the reliability of real-time information and the specificities of the scenario, P... base Corrections are made: if real-time traffic or parking data experiences network latency exceeding 2 minutes, the weight of the corresponding feature is reduced; if the weather forecast indicates moderate to heavy rain, it is multiplied by a significantly reduced weather factor; if the prediction is for a longer period, a confidence factor that decays over time is introduced. This is achieved using the formula C=P. base W traffic W parking W weather W freshness The final demand forecast confidence level C is calculated using +Bias, where Bias is a bias term that is dynamically adjusted based on recent forecast accuracy and is used to calibrate the overall bias of the model.

[0069] The system maintains a user-personalized activation threshold T, with an initial value that can be set to 0.7 (70%). The system tracks a short-term predictive trigger record: When the confidence level C > T, the system triggers preprocessing and provides suggestions, recording the user's feedback on these suggestions (acceptance / ignoring).

[0070] Calculate the user adoption rate R in the short term: R = (number of adoptions) / (total number of suggestions triggered).

[0071] If R > 0.6, it indicates accurate prediction and user satisfaction; T can be slightly lowered (e.g., to 0.65) to make the system more aggressive. If R < 0.3, it indicates inaccurate prediction or user disruption; T should be increased (e.g., to 0.75) to make the system more conservative. Adjustments should be made in small increments (e.g., ±0.05) with upper and lower limits, such as 0.5~0.85, to avoid oscillations.

[0072] Based on the predicted retrieval time T retrieve and the outside temperature T out And the suitable operating temperature range of the bicycle battery, the decision target warehouse temperature T target The central control unit commands the integrated PTC heater inside the chamber to start, employing closed-loop PID control, with a temperature sensor providing real-time feedback on the chamber's internal temperature T. in The controller dynamically adjusts the duty cycle of the PTC, so that T in In T retrieve Always stable at T target If the outside temperature is high, the system will activate the ventilation fans and may even introduce cool air from the vehicle's air conditioning system through the ducts.

[0073] Through the established charging connection, the vehicle's battery management system maintains periodic communication with the electric bicycle's battery management system to obtain the battery core temperature T. cell If T cell <10°C, and is predicted to be used. The vehicle battery management system commands the individual vehicle battery management system to activate its internal heating film, or apply a small DC bias current through the charging circuit. Joule heating is generated using the battery's internal resistance, with the temperature rise rate controlled at 1-2°C / minute to prevent thermal runaway. The goal is to keep the temperature below 10°C. cell Increase to above 15°C. If T cell The temperature is above 35°C, and it is predicted that the system will be used or is currently charging. The system may reduce charging power to reduce heat generation, while simultaneously instructing increased ventilation within the chamber, with the goal of reducing the temperature of the T-cell. cell Maintain a safe temperature range below 40°C.

[0074] In some optional embodiments, the operation corresponding to the physical holding strategy is to dynamically adjust the clamping force of the locking mechanism in the closed storage compartment on the electric bicycle according to the bumpiness of the road surface, or to adjust the damping coefficient of the shock-absorbing element in the closed storage compartment. The operation corresponding to the charging management strategy is to control the electric bicycle battery to discharge to the vehicle's low-voltage electrical system when it is detected that the vehicle is in a parked state and its low-voltage battery power is lower than a first set value, while the electric bicycle battery power is higher than a second set value.

[0075] An example of implementing the physical preservation strategy adjustment steps is as follows: Vertical acceleration signal a from the vehicle's inertial measurement unit was acquired at a frequency of 100Hz. z (t), and four wheel speed signals. For a z The (t) signal is bandpass filtered, for example, to 4-12Hz, as this frequency band contains most of the vehicle vibration energy caused by road surface irregularities. The root mean square value of the filtered signal within the sliding time window is calculated as the real-time bump index I. b At the same time, analyzing the instantaneous rate of change of wheel speed signals can provide early warning of large impacts that a single wheel is about to encounter, such as going over a pothole.

[0076] The bump index I was established in advance through real vehicle calibration. b With recommended clamping force F rec The mapping table. The foundation force F corresponding to a straight highway. min For example, 400N corresponds to the maximum safety force F on unpaved roads. max For example, 1800N. The central control unit determines the current I... b F is obtained by looking up the table. rec The target force is then sent as the target value to the servo driver of the locking mechanism. The driver operates in torque mode, and its internal current loop controls the target current calculated from the target force. Strain gauge force sensors on the locking tongue form the outer feedback loop. A PI controller fine-tunes the current command based on the difference between the target force and the actual feedback force, achieving precise and stable tracking of the clamping force. The adjustment process uses a ramp function to avoid abrupt changes in the force value.

[0077] If the storage compartment is connected to the vehicle body, a magnetorheological damper is installed. The central control unit, according to I... b And the vehicle driving mode, determine the target current I by consulting another table. mr The damping force of the MR shock absorber is related to I. mr Approximately proportional. In Comfort driving mode, I mr Follow me b Slower growth, maintaining gentle damping; in Sport driving mode, base I mr Higher, providing a tighter road feel.

[0078] Examples of implementing on-board energy sharing steps are as follows: A bidirectional DC-DC converter and a high-voltage relay set are added to the vehicle's electrical architecture. The high-voltage side of the bidirectional DC-DC converter is connected to the output terminal of the electric bicycle battery, and the low-voltage side is connected to the vehicle's battery and low-voltage load network. The high-voltage relay set is used to connect or disconnect the electric bicycle battery from the DC-DC converter. Understandably, necessary current Hall sensors and voltage sampling circuits can also be added to the vehicle's electrical architecture.

[0079] In this embodiment, the system runs a low-power background monitoring task to continuously check the vehicle's power status (OFF position, vehicle speed 0), gear position (P position), and handbrake status (engaged) to confirm that the vehicle is in a safe parking state. The system reads the state of charge (SOC) of the 12V battery through the vehicle battery management system. veh The power-assisted bicycle's battery management system reads the battery's state of charge (SOC). bike If SOC veh <First set value (e.g., 28%), SOC bike If the second set value (e.g., 55%), the system is not performing any access operations, and there is no immediate user instruction, the central control unit will first instruct a self-test, which checks the status of the DC-DC converter, the status of the relay contacts, and the insulation resistance of the lines.

[0080] First, close the low-voltage side relay. The DC-DC converter operates at a low duty cycle, and the output voltage rises slowly to pre-charge the high-voltage side capacitor, preventing inrush current surges. After pre-charging is complete, close the high-voltage side relay.

[0081] Central control unit based on SOC veh The degree of power depletion is identified, and a discharge power P is set by looking up a table or using a simple formula. discharge For example, SOC veh Set to 200W at 25%, SOC veh Set to 250W at 20%. The instruction DC-DC converter operates in constant power mode, drawing power from the electric bicycle battery, converting it to 12V, and supplying it to the vehicle's low-voltage network, primarily for charging the battery.

[0082] Real-time monitoring of discharge current, voltage, battery temperature, and DC-DC converter temperature; if any parameter exceeds the limit, such as the electric bicycle battery temperature >45°C or abnormal current, the protection shutdown will be triggered immediately.

[0083] Mutual aid shall cease when any of the following conditions are met: SOC veh Rebound to the target value, such as 45%; SOC bike The voltage drops to a protection level, such as 25%; the user operates the vehicle, such as unlocking or opening a door; the system receives an access command. The shutdown sequence is as follows: first, the DC-DC converter is instructed to reduce its output power to zero; then, the high-voltage side relay is disconnected; and finally, the low-voltage side relay is disconnected.

[0084] In some optional embodiments, during the process of controlling the on-board automatic access mechanism to perform the electric bicycle access action corresponding to the access operation command or access request, the following steps are performed simultaneously: By fusing data from at least two different types of sensors located around the access port, a dynamic environment model outside the access port is constructed in real time. Identify potential risk elements in the dynamic environment model, including moving objects and low, stationary obstacles; Risk assessment is performed based on the type of identified risk factors, their trajectory, and the current motion state of the automated access mechanism. Based on different risk assessment levels, corresponding graded safety responses are executed, including: reducing movement speed and enhancing warnings, or immediately suspending movement and activating audible and visual alarms.

[0085] In this embodiment, an ultra-wide-angle high-definition camera is installed on the upper edge of the access door, covering a fan-shaped area in front of and to the side of the access opening. It is primarily used for object detection, classification (people, vehicles, bicycles, pets), and semantic segmentation (distinguishing between roads, grass, and obstacles). A short-range millimeter-wave radar is installed on each side of the access door, with a detection range of 0.1-10 meters and high angular resolution. The radar beam is focused on the moving area of ​​the access mechanism, accurately measuring the distance, radial velocity, and angle of all objects within the area. It is unaffected by light or rain / fog and can penetrate non-metallic thin plates to detect partially obstructed objects. Six to eight ultrasonic sensors are evenly spaced along the lower edge of the access door. Each sensor has a detection range of 0.1-2 meters, offering high accuracy and low cost. These sensors are specifically designed to detect low, stationary obstacles close to the ground and to detect whether the space below the extended slide is encroached upon.

[0086] Data fusion and dynamic environment modeling are performed by a high-performance core fusion algorithm within an environmental perception ECU or central control unit, as detailed below: All sensor data is synchronized in time via hardware timestamps or software interpolation and converted to a unified vehicle coordinate system.

[0087] Visual detection algorithms extract target bounding boxes and categories from images. Millimeter-wave radar provides point cloud clusters and velocity vectors for the targets. Fusion algorithms correlate and match visually detected bounding boxes with radar point cloud clusters. For successfully matched objects, attributes such as category, location, and velocity are determined complementaryly by visual and radar data, resulting in higher reliability. Ultrasonic data is primarily used to construct near-ground 2D occupancy grid maps, identifying impassable areas.

[0088] The final result is a real-time dynamic environment model, which includes a 2D / 2.5D occupancy grid map and a dynamic target list. Each target in the dynamic target list contains: ID, category, confidence score, location coordinates (x, y), velocity vector (vx, vy), acceleration, and predicted trajectory.

[0089] The identification of potential risk factors and the quantification of risk assessment are illustrated in the following examples: First, risk factors can be defined as categories I to III. Category I represents invasive moving objects whose predicted trajectories intersect with the current or future planned movement area of ​​the access mechanism. Based on speed and direction, it can be further subdivided into fast intrusion and slow intrusion. Category II represents invasive stationary obstacles, i.e., stationary objects that are already located or partially located within the movement area of ​​the access mechanism. Category III represents ground hazards, which are protrusions or pits identified by ultrasonic grid maps that exist on the expected landing point or movement path of the slide.

[0090] For each dynamic target i, calculate its shortest predicted collision time TTCi with the motion envelope of the access mechanism. TTCi = shortest distance from target i to the edge of the motion envelope / normal velocity component of target i toward the motion envelope. If the normal velocity component is negative (i.e., moving away), then TTCi is set to infinity.

[0091] Comprehensive risk score, i.e., Risk Score_i =f(Category Weight, TTCi, Target Motion Uncertainty). Here, the category weights are preset, for example: Child / Pet weight = 1.5, Adult weight = 1.0, Cycling weight = 0.8. The shorter the TTCi, the higher the score. Motion uncertainty adds points.

[0092] Static risk scoring assigns a static risk value based on the overlap area between stationary obstacles and the moving envelope, as well as the severity of ground hazards.

[0093] The system is based on the highest risk among all current risk factors. Score Based on static risk values, the global risk is divided into four levels: Level 0: Safe; no risk factors, or all TTCi > 8 seconds and no static risks.

[0094] Level 1: Note; the highest risk score is low, or there is static risk but it is far away.

[0095] Level 2: Warning; There is a clear moderate risk, for example, an object is approaching at a moderate speed, TTCi between 2 and 5 seconds; or a stationary obstacle is less than 20 cm from the edge of the moving area.

[0096] Level 3: Emergency; High risk exists, for example, an object is rapidly moving towards the moving area, TTCi < 2 seconds; or any object has entered the moving area.

[0097] The safety monitoring module sends the assessed risk level to the motion control module and the body control module in real time, triggering a coordinated response: Level 1 response: The instruction access mechanism enters a low-speed caution mode, the maximum speed of the slide movement is limited to 50% of the normal value, and the acceleration is halved; the instruction activates a Level 1 warning.

[0098] Level 2 response: Instruct the access mechanism to slow down or pause, immediately reduce the current movement speed to a very low level (e.g., 10%), or pause the start if the critical phase has not yet begun; Instruction activates a Level 2 warning, continuously monitors the risk, and if the risk is resolved within 2 seconds (TTCi increases or the target leaves), automatically downgrade to Level 1 and resume movement; If the risk persists, remain paused.

[0099] Level 3 Response: The command access mechanism applies emergency braking, and all drive motors immediately apply maximum braking torque to stop the moving parts within the shortest distance; the command activates a Level 3 emergency alarm; Level 3 risks must be manually confirmed for deactivation. If the system detects that the risky target has completely left the danger zone for more than 5 seconds, and the user has not performed any operation, a pop-up window may automatically request the user to confirm the restoration. After restoration, the user can choose to continue or cancel the current operation.

[0100] In some optional embodiments, performing the electric bicycle storage action includes: Control the opening of the closed storage compartment door and move the support part of the automatic storage mechanism to the external preparatory position; After the electric bicycle is placed in the support unit and locked, the support unit is retracted into the closed storage compartment and the compartment door is closed. After the supporting part is retracted into place, the charging interface located in the enclosed storage compartment is automatically connected to the charging interface of the electric bicycle, and charging is started.

[0101] In this embodiment, the control sequence for storing the electric bicycle is as follows: Opening the compartment door: The central control unit unlocks the electric compartment door lock via the LIN bus. Then, it sends a position command to the compartment door drive motor controller, controlling the door to smoothly open to the preset maximum opening degree.

[0102] The support unit extends to the external preparatory position: Upon receiving the door positioning signal, the central control unit sends an absolute position command to the servo drive system of the automatic storage mechanism via the CAN bus. The servo drive controls the servo motor, which, through a ball screw pair, drives the slide carrying the electric bicycle to move outward from the storage origin within the compartment along a linear guide rail. The absolute encoder built into the slide provides real-time position feedback, forming a fully closed-loop position control. When the front end of the slide reaches the pre-calibrated external preparatory position, the motor stops and enters the position holding mode.

[0103] E-bike placement and automatic locking: After the slide is in position, the e-bike is pushed onto it, and multiple pressure sensors and photoelectric sensors integrated inside the slide begin to operate. When a weight exceeding a threshold is detected and evenly distributed in a specific area, and the photoelectric sensors are obstructed by the e-bike frame, the system determines that the e-bike is initially in place. Subsequently, the central control unit instructs the locking and docking devices on the slide to activate.

[0104] Carrier retraction and door closing: After detecting successful locking, the system automatically waits for a short delay before triggering the next step. The central control unit instructs the servo motor to rotate in the opposite direction, and the slide carrying the locked e-bike smoothly retracts into the compartment along the original path. When the slide triggers the reed sensor storing the origin, it confirms that the bike has been fully retracted. Subsequently, the central control unit instructs the door drive motor to close the compartment door.

[0105] Automatic charging interface docking and charging initiation: The position of the slide at the storage origin is strictly calibrated in three dimensions. After the slide retracts into place and a signal confirms this, the central control unit instructs the charging docking mechanism to begin operation. The charging status is displayed in real time on the vehicle's infotainment screen.

[0106] The control sequence for retrieving the electric bicycle is as follows: After the safety verification is passed, the central control unit commands the compartment door to open.

[0107] The command slide carries the electric bicycle from the storage origin to the external ready position.

[0108] Once the slide reaches its designated position, the locking device is released. The micro motor rotates in the opposite direction, retracting the latch. A signal confirming the complete retraction of the latch is received by another microswitch.

[0109] The LED lights on the slide change their flashing pattern, helping to push the bicycle off the slide.

[0110] Once the sensors on the slide detect the removal of the load, they send a feedback signal. The central control unit then controls the slide to automatically retract into the compartment, and then closes and locks the compartment door.

[0111] In some optional embodiments, the method further includes: Monitor and record the storage status, battery health status, and data from each access and charge / discharge cycle of the electric bicycle. Based on the time series of the recorded access and charge / discharge data, the battery degradation trend is analyzed, and combined with the storage status, a maintenance suggestion report containing maintenance reminders or fault warnings is generated and pushed to the user terminal.

[0112] Each time an e-bike is stored, a storage start event log is recorded, including a timestamp, vehicle location, and initial temperature / humidity inside the storage compartment. During storage, the average temperature and humidity inside the compartment are recorded at a lower frequency. When the vehicle is in motion, the vehicle's mileage data and inertial measurement unit data used to calculate the bump index are correlated to estimate the vibration load experienced by the e-bike during that storage cycle.

[0113] At the beginning and end of each charging cycle, the vehicle's battery management system requests and records the following key health parameters from the electric bicycle's battery management system: the percentage of total battery pack capacity degradation, the maximum voltage difference between individual cells within the battery pack, and the battery's DC internal resistance. These data directly reflect the battery's health status.

[0114] Each access operation records: operation type, trigger source, time, location, and result status.

[0115] Each charging event records: start time, start state of charge, end time, end state of charge, cumulative charge amount, average charging power, charging mode, and highest battery temperature during charging.

[0116] Record the following for each discharge event: start time, start state of charge, end state of charge, discharge amount, and average discharge power.

[0117] All recorded data is stored in encrypted form in the memory of the central control unit or vehicle gateway, forming a local time-series database. When the vehicle has a network connection, this data is synchronized to a dedicated database in the cloud linked to the user account for long-term storage and in-depth analysis.

[0118] The data analysis engine primarily runs on cloud servers, periodically analyzing all user data for all e-bikes. It can also be triggered by specific events, such as an abnormally extended charging time.

[0119] For example, data analysis is performed using a battery degradation trend analysis algorithm, as follows: Extract all historical battery health status snapshot data points and sort them by time.

[0120] By fitting the data using a linear regression or exponential decay model, a trend line is obtained showing how the battery health status changes over time. The slope of the trend line is then calculated, which represents the percentage of capacity decay per month.

[0121] The calculated degradation rate is compared with the standard degradation curve of the battery model. If the user's actual degradation rate is consistently higher than a certain threshold of the standard curve, such as 1.5 times, it is marked as abnormal accelerated degradation.

[0122] The degradation was correlated with charging behavior and storage environment to identify possible causes.

[0123] The total estimated cycling mileage, number of accesses, and average single cycling distance within the statistical period.

[0124] The system displays the current battery health status, compares it with the previous report, analyzes the degradation trend, and provides personalized suggestions based on the analysis. The data shows that the battery was stored after the charge level dropped below 10% on several occasions recently, which is recommended to be avoided as much as possible to extend battery life.

[0125] Based on mileage or time thresholds, for example, when the system records that the total mileage of the e-bike is close to 500 kilometers, the report will prompt: Your e-bike drive chain is close to the recommended maintenance mileage, and cleaning and lubrication are recommended.

[0126] If data analysis reveals potential issues, such as a gradual increase in cell voltage consistency during the last three charges, the report will issue a warning: "A downward trend in battery pack voltage consistency has been detected. We recommend visiting a service center for inspection soon."

[0127] Based on the storage status data, if it is found that the e-bike has been stored in a high-temperature environment (>35°C) for a long time, the report will indicate: "In the past month, the e-bike has been in a high-temperature environment for a long time. Please check the tires and plastic parts for signs of aging."

[0128] The generated HTML or PDF report is pushed to the vehicle's infotainment system via the vehicle network and a notification message with detailed information is sent to the associated mobile app. Users can view the full report in the app, which includes one-click links for scheduling maintenance services or contacting customer service. The system records whether users have viewed the report, providing a reference for improving service delivery.

[0129] In some optional embodiments, the control of the on-board automatic access mechanism to perform the assistive bicycle access action corresponding to the access operation command or the access request further includes: Real-time acquisition and fusion of environmental perception data, vehicle status data, and command priority information to form a comprehensive decision dataset; Based on the comprehensive decision dataset, the optimal motion control sequence of the automatic access mechanism is dynamically generated. The optimal motion control sequence includes the planning of the movement path and speed curve and its coordination with the execution timing of the preprocessing operation. Based on the optimal motion control sequence, the automatic access mechanism is controlled to perform access actions.

[0130] In this embodiment, the construction method of the comprehensive decision dataset is exemplified as follows: The system acquires environmental perception data and directly uses the dynamic environmental model output by the intelligent safety monitoring process for execution. The scheduler in the model pays special attention to: whether there are any newly added minor obstacles on the planned path of the slide in the static occupation grid map; and whether there are any targets in the dynamic target list whose predicted trajectories overlap with the movement of the slide in time, even if their predicted collision time has not yet reached the high-risk level, but may affect the selection of the optimal path.

[0131] The system acquires vehicle status data, specifically by sampling the vehicle's 12V battery voltage in real time via an analog-to-digital converter. A low voltage indicates a heavy grid load or poor battery condition. The dispatcher then needs to decide to reduce the peak current of the access mechanism's motor and use a smoother acceleration curve to avoid sudden voltage drops that could cause other ECUs to reset.

[0132] The vehicle's attitude is obtained, specifically the pitch and roll angles from the inertial measurement unit. If the vehicle is parked on a slope, the slide needs to compensate for the gravitational component in addition to overcoming friction and inertia during extension and retraction. The dispatcher needs to add a feedforward compensation term to the torque command.

[0133] The system obtains the vehicle's load status, specifically from information obtained from the air suspension or vehicle height sensors. If the vehicle is fully loaded, the body is lowered, which may reduce the relative height of the compartment opening to the ground by a few centimeters. The dispatcher needs to adjust the final height setting of the external preparatory position of the slide accordingly to ensure the smooth placement of the assisted bicycle.

[0134] Obtain instruction priority information; specifically, the system defines a four-level instruction priority system. P0 (highest security), emergency stop command, originating from security monitoring Level 3 or the user's emergency button.

[0135] P1 (User Immediate) refers to the access command that the user has just issued through any interface.

[0136] P2 (System Preparation): The system autonomously generates preparation instructions based on high-confidence predictions.

[0137] P3 (Background Maintenance) refers to system-initiated self-test, calibration, or maintenance relocation commands.

[0138] The dynamic programming algorithm for the optimal motion control sequence is executed as follows: Based on a standard straight path, if environmental perception data shows small, evasive, stationary obstacles on the path, and the sliding mechanism has slight lateral freedom, the motion planner in the scheduler will run a dynamic window method to plan a smooth curved path around the obstacle, taking into account the mechanism's kinematic constraints. If lateral avoidance is not possible, the planner may plan a complex sequence of movements: first lifting, then crossing, and then descending.

[0139] Multi-objective velocity curve optimization is performed, that is, the planner optimizes the velocity curve based on a weighted cost function J=w1. T+w2 E+w3 Jerk is used to optimize the velocity profile. Here, T is the estimated total time, E is the estimated energy consumption related to the integral of the square of the current, and Jerk is the integral of the jerk reflecting stability. Weighting coefficients w1, w2, and w3 are dynamically adjusted according to the current mode, for example: In standard mode, a balanced setting is used, for example, w1=1, w2=0.5, w3=1.

[0140] In Quiet Mode, w3 is significantly increased, for example, w3=3, using a very smooth S-shaped curve with very small acceleration and deceleration.

[0141] In Speed ​​Mode, users click rapidly in quick succession, increasing w1 (e.g., w1=2), allowing for greater acceleration and maximum speed.

[0142] In energy-saving mode, when the vehicle's battery is low, increase w2 (e.g., w2=2) to adopt the most economical speed curve, which may sacrifice some time.

[0143] Coordinated planning with the execution timing of preprocessing operations is the core of intelligent scheduling. The scheduler monitors the progress of all background tasks. For example, if the battery preheating task requires 60 seconds to reach the target temperature, while a complete quick retrieval operation takes 20 seconds, and a retrieval command is issued 50 seconds after preheating begins, the scheduler will perform a rapid calculation: Option A: Start picking up the car immediately, and it will be completed in 20 seconds. However, at this time, the battery temperature may only rise from -5°C to 5°C, which is not optimal.

[0144] Option B: Delay the vehicle retrieval process by 5 seconds, and simultaneously briefly increase the preheating power. This way, the vehicle retrieval process will be completed at 25 seconds, and the battery temperature may reach 8°C at 25 seconds, which is better.

[0145] The scheduler selects option B based on a simple rule, such as prioritizing the temperature to rise above 5°C when the battery temperature is below 0°C, and generates a corresponding motion control sequence that is synchronized with the preprocessing progress.

[0146] The optimal motion control sequence generated by the plan is a high-resolution time-position-velocity-current setpoint curve, which is stored in the internal memory of the slide servo drive. The drive employs an advanced control strategy of position feedforward + velocity feedforward + current feedback to ensure that the actual motion trajectory closely follows the planned trajectory, while also providing good suppression of sudden load disturbances. Throughout the execution process, the scheduler continues to monitor the input data, and if a higher-priority event occurs, it immediately aborts the current sequence and switches to a safer processing sequence.

[0147] In some optional embodiments, the operations corresponding to the physical retention strategy further include: Continuously monitor the vehicle's dynamic driving parameters and predict potential severe impact risks based on parameter change trends; When the predicted risk exceeds a preset threshold, before the actual impact occurs, the locking mechanism of the closed storage compartment is actively controlled to enhance its force, and / or the electrical connection path between the electric bicycle battery and the vehicle charging system is cut off.

[0148] In this embodiment, the algorithm for predicting severe impact risk is as follows: Monitoring parameters are acquired at a frequency of 100Hz or higher: longitudinal acceleration a x Brake master cylinder pressure P brake Four wheel speeds ω fl ω fr ω rl ω rr and the front suspension travel sensor signal S f .

[0149] Calculate a x Short-term trends in weather patterns are used to predict emergency braking. When a x It is negative and its absolute value increases rapidly, while P brake If the rate of pressure rise exceeds a high threshold, such as 500 bar / s, the system predicts that an emergency braking event with a deceleration exceeding 0.7g will occur within the next 200-400 milliseconds.

[0150] Predictive models may employ simple linear extrapolation or more complex algorithms based on vehicle dynamics models and driver intent recognition.

[0151] Under the active protection mechanism, a predicted impact intensity threshold is first set. This threshold is determined through actual vehicle calibration and corresponds to the impact level that may damage the bicycle's fixation or electrical connections. When the predicted impact intensity output by the prediction algorithm exceeds the threshold, the central control unit immediately sends a peak force command to the locking mechanism servo driver. This command bypasses the normal PI force control loop and directly requires the driver to output the maximum permissible continuous current I within a short period of time. max_contThe corresponding torque. Driven by peak current, the servo motor rapidly increases the clamping force from the current adaptive value to the mechanical and motor limits. This overlock state is established before the actual impact force arrives, providing maximum holding force to resist large inertia. After the impact event, the system gradually and smoothly restores the clamping force command to a level adapted to the current road surface bump index. Almost simultaneously with sending the peak force command, the central control unit sends an emergency disconnect command to the charging management module via a high-priority, high-speed CAN message. The charging management module controls the fast-break relay at the charging interface to immediately disconnect.

[0152] Under severe impact, even slight movement of the mechanical interface can cause relative displacement between the hard-connected electrical contacts, leading to contact wear, arcing, or even short circuits. Premature disconnection eliminates this risk. It also prevents the impact of a sudden surge of current generated inside the battery connector during the impact on the battery management system.

[0153] After the impact, the system checks the status of the charging port. If the vehicle is still in motion, it remains disconnected. If the vehicle stops and needs charging, the system attempts to restart the automatic docking process, as the port may have become misaligned after the severe impact and needs to be realigned.

[0154] In some optional embodiments, when the electric bicycle is in its onboard storage state, the collaborative charging strategy for the electric bicycle is adaptively adjusted according to dynamic conditions. The operation corresponding to the collaborative charging strategy is as follows: The battery of the electric bicycle is used as a controllable energy storage node in the vehicle's integrated energy management network. Based on the overall operating conditions of the vehicle, external power grid signals, user-preset scenario modes, and the priority of each power-consuming unit, the system collaboratively decides the charging and discharging timing and power of the electric bicycle battery in different scenarios such as vehicle driving, parking, or external power supply through a predetermined energy allocation strategy model.

[0155] In this embodiment, the implementation of the collaborative charging strategy relies on the vehicle's top-level energy management strategy. The electric bicycle battery reports its status to the vehicle's energy management controller via the central control unit. The vehicle's energy management controller registers the electric bicycle battery as a controllable energy storage node on par with other energy storage units in its energy network topology. Therefore, charging and discharging the electric bicycle battery is no longer an independent action, but rather part of the global optimization of the vehicle's energy management controller.

[0156] The overall operating conditions of the vehicle mentioned above include driving status, charging status, and external power supply status.

[0157] Examples of driving status include: Is the vehicle in motion? Is the engine / drive motor operating? What is the state of charge and available power of the high-voltage battery? Is the vehicle in energy recovery mode? Examples of charging status include: Is the vehicle connected to an external charging station? What is the rated power of the charging station? Examples of external power supply status include: Is the vehicle using vehicle-to-load or vehicle-to-grid functionality? What is the power requirement for external power supply? The aforementioned external power grid signals include obtaining time-of-use electricity price information through charging pile communication, or receiving demand response signals from the power grid dispatch center, requesting a reduction or suspension of charging during specific periods.

[0158] The aforementioned user-preset scenario modes refer to the global energy preference modes that users can select in the vehicle's infotainment system, such as: Daily commuting mode: Prioritizes minimizing vehicle usage costs and prefers charging during off-peak electricity hours.

[0159] Long-distance travel mode: Prioritizes the charging power of the vehicle's main battery and limits the charging power of auxiliary devices.

[0160] Camping / Outdoor Mode: Prioritizes the duration and power of the vehicle's power supply to the load, allowing the electric bicycle battery to be used as a backup power source.

[0161] Maximize protection mode: Prioritizes battery life and avoids charging in extreme temperatures or under high charge conditions.

[0162] The vehicle energy management controller maintains a dynamic priority table for electrical equipment, with priorities changing according to operating conditions.

[0163] The vehicle energy management controller incorporates a strategy matrix or rule engine for different scenario combinations. The following example illustrates this: Scenario A: The vehicle is in motion, the high-voltage battery has sufficient charge (SOC > 75%), and the user mode is daily commuting. The vehicle energy management controller determines that the vehicle has surplus energy and decides to allow charging of the electric bicycle battery. Charging power P charge Based on a lookup table, for example, for every 5 percentage points above 75% in the high-voltage battery's state of charge, increase the charging power by 200W, but not exceeding the maximum acceptable power of the electric bicycle battery and the vehicle's surplus power generation / discharge capacity. The goal is to charge the electric bicycle battery to the user-set daily target state of charge.

[0164] Scenario B: The vehicle is connected to a home AC slow charging station (7kW). Currently, it's off-peak electricity pricing at night. The vehicle's high-voltage battery has a State of Charge (SOC) of 60%, and the electric bicycle's battery has a SOC of 30%. The decision is: the vehicle's energy management controller adopts a cost-priority, parallel charging strategy. Because off-peak electricity is cheaper, and both batteries need charging, the vehicle's energy management controller may allocate 4kW to charge the high-voltage battery and 2kW to charge the electric bicycle's battery. Once the electric bicycle's battery is fully charged, the entire 7kW will then be used for the high-voltage battery.

[0165] Scenario C: The vehicle is in camping mode, using V2L to power an external device on a bicycle at 2kW. The vehicle's high-voltage battery's State of Charge (SOC) has dropped from 100% to 65%. The decision is as follows: To extend the total power supply time, the vehicle's energy management controller initiates a hybrid power supply strategy. When the high-voltage battery's SOC drops to 60%, the vehicle's energy management controller instructs the system to connect the bicycle's battery discharge path. Through DC-DC conversion, the battery's energy is connected to the vehicle's high-voltage DC bus at 1kW, supporting the 2kW V2L load along with the high-voltage battery. This extends the total power supply time by several hours.

[0166] Scenario D: The vehicle will make a reservation via the internet and connect to a DC fast charging station for rapid charging in half an hour. The decision is as follows: The vehicle energy management controller decides not to charge the electric bicycle's battery before connecting to the fast charging station. Furthermore, if the electric bicycle's battery has a high charge level, such as >80%, it may consider transferring some of its charge to the vehicle's high-voltage battery. This ensures that during fast charging, all the valuable fast charging power is used for the high-voltage battery, shortening the overall charging time.

[0167] The decisions made by the vehicle energy management controller are translated into specific control commands, which are sent to actuators such as the central control unit, vehicle battery management system, and on-board charger via the CAN bus. The central control unit is responsible for executing the specific charging and discharging control of the electric vehicle's battery and feeding back the execution status and results to the vehicle energy management controller in real time, forming a closed-loop management system. This global collaborative charging strategy achieves optimal energy scheduling across energy storage units, improving the vehicle's energy economy, flexibility, and intelligence.

[0168] Exemplary system In one exemplary embodiment of this application, a vehicle-mounted power-assisted bicycle integrated system 200 is also provided; please refer to [link to relevant documentation]. Figure 2 , Figure 2The following is a structural block diagram of an integrated system for a vehicle-mounted electric bicycle provided in one embodiment of this application. The system 200 includes: a closed storage compartment 201, which is constructed inside the vehicle body; an automatic storage and retrieval mechanism 202, which is disposed in the storage compartment; a vehicle domain controller 203, which is disposed in the vehicle; and a central control unit 204, which is communicatively connected to the vehicle domain controller 203 and the automatic storage and retrieval mechanism 202.

[0169] The central control unit 204 is configured to collaboratively execute access control processes and intelligent management processes, specifically configured as follows: Responding to access commands for the vehicle-mounted electric bicycle or access requests generated by the system itself; The process of performing coordinated access control includes verifying whether the vehicle is in a safe state that allows access, and controlling the automatic access mechanism to perform the storage or retrieval operation of the electric bicycle after the verification is passed; The intelligent management process is executed collaboratively, and the intelligent management process interacts with the access control process through data exchange and instruction coordination, and is any one or a combination of the following: The demand forecasting module within the central control unit performs potential usage demand forecasting based on multi-source data and triggers preprocessing operations based on the forecast results. The adaptive management module within the central control unit performs adaptive adjustments to the charging management strategy or physical holding strategy based on dynamic conditions when the electric bicycle is in the onboard storage state.

[0170] The enclosed storage compartment 201 is not an aftermarket addition, but rather an integral part of the vehicle's body-in-white design or deeply integrated as a modular assembly. Its preferred location is the tailgate area or the lower part of the trunk. In the tailgate design, the inner panel uses a reinforced structure to create the storage space, while the outer panel employs a variable-shape design, such as a retractable / flip-out panel driven by a built-in shape memory alloy or a micro-motor. This automatically fills in any dents after the electric bicycle is stored, maintaining a smooth surface and optimizing aerodynamics.

[0171] The enclosed storage compartment 201 is made of double-layered steel plates sandwiched with sound and heat insulation materials, providing excellent sealing. Internally, it integrates a temperature sensor, a humidity sensor, a heater, and a brushless DC fan. These environmental control components are directly controlled by a central control unit to perform pretreatment operations.

[0172] The rear wall of the enclosed storage compartment 201 is fixed with a mounting base and a floating platform for the charging docking mechanism, ensuring a precise relative position between the charging interface of the electric bicycle on the automatic storage and retrieval mechanism 202.

[0173] The automatic storage and retrieval mechanism 202 includes a guide rail and frame, a support slide, a locking and docking device, a drive unit, a charging docking mechanism, and a sensor network.

[0174] The guide rail and frame are constructed from high-strength aluminum alloy profiles and connected to the skeleton of the enclosed storage compartment 201 via shock-absorbing rubber pads. Precision linear guide rails are fixed to the frame, serving as the motion reference for the slide table.

[0175] The support slide, made of carbon fiber composite material or lightweight alloy, is the component that comes into direct contact with the e-bike. Its upper surface has anti-slip textures and guide ridges, and it contains multiple thin-film pressure sensors for detecting the placement of the e-bike and a set of photoelectric sensors for detecting the frame's positioning.

[0176] The locking and docking device is integrated at the front end of the slide and includes: a waterproof and dustproof DC brushless motor, a reduction worm gear mechanism, two retractable alloy steel locking tongues, strain gauge force sensors at the ends of the locking tongues, and two limit microswitches. The locking and docking device is responsible for engaging with the standardized mechanical interface on the bottom of the electric bicycle.

[0177] The drive unit includes a servo motor with a high-resolution absolute encoder, a precision ball screw pair, and a matching servo driver. The servo driver receives position / speed / torque commands from the central control unit 204 via a CAN bus and provides feedback on the motor status and actual position.

[0178] The charging docking mechanism is an independent active alignment mechanism, comprising a two-dimensional (XY) passive floating platform consisting of crossed roller guides and springs, a Z-axis miniature linear motor, and a standard electric vehicle charging socket mounted on the platform. The miniature linear motor, controlled by the central control unit 204, pushes the socket forward / backward to complete docking and separation.

[0179] The sensor network includes a slide absolute encoder, a lock-up status switch, and a charging connection sensor. The sensor signals from each node are aggregated through an I / O board or sub-controller and then uploaded to the central control unit 204.

[0180] The central control unit 204 utilizes an automotive-grade multi-core microcontroller with ample computing power, memory, and various communication interfaces. Its software employs a modular design based on real-time operating systems such as AUTOSAR or OSEK / VDX, and is specifically configured as follows: To respond to access requests: The communication driver module listens for CAN / LIN / Bluetooth messages from the user interface and software flags from the internal demand forecasting module, and performs unified priority scheduling and management for both.

[0181] To implement the coordinated access control process: The system interacts with the vehicle domain controller 203 via the CAN bus, periodically acquiring and parsing vehicle status messages. A state machine is run to perform logical AND operations on the aforementioned safety conditions.

[0182] The central control unit 204 internally operates a high-performance motion control core, which sends PVT (position-speed-time) setpoints or direct position commands to the servo driver via the CAN bus according to the planned motion sequence. Simultaneously, it controls the door motor, locking motor, and charging docking linear motor via the LIN bus, and reads feedback from all sensors in real time for closed-loop control and fault diagnosis.

[0183] The following functional modules in the central control unit 204 implement the collaborative execution of the intelligent management process, specifically: The demand forecasting module runs a lightweight machine learning inference engine that loads a personalized travel habit profile model for the vehicle's users, such as a decision tree model file, delivered from the cloud. Simultaneously, it obtains real-time dynamic information from the vehicle navigation and vehicle-to-everything (V2X) control units via an application programming interface (API). This module periodically performs matching calculations, generates confidence scores, and compares them with dynamic thresholds. If a condition is triggered, preprocessing instructions are generated and placed into the internal task queue of the central control unit.

[0184] The adaptive management module contains multiple sub-threads. Among them, the physical holding strategy thread subscribes to inertial measurement unit data from the vehicle bus, calculates the bump index in real time, looks up the target clamping force from a table, and sends it to the servo driver of the locking device or the independent motor controller via CAN. The energy mutual assistance control thread monitors the battery state of charge of the vehicle and the electric bicycle. When the conditions are met, this thread executes the complete mutual assistance process by controlling the CAN commands of the relevant relay drivers and DC-DC converters. The cooperative charging strategy agent module acts as a client of the vehicle energy management controller. It is used to periodically report the status of the electric bicycle battery to the vehicle energy management controller and receive charging and discharging commands from the vehicle energy management controller. Then, it converts these commands into specific control commands for the charging system and the electric bicycle battery management system.

[0185] The central control unit 204 enables data interaction and command coordination. Specifically, in terms of software architecture, it adopts a publish-subscribe pattern or a shared memory mechanism. For example, when the access control module starts moving the slide, it publishes a motion status message. Both the demand forecasting module and the adaptive management module subscribe to this message. Upon receiving the message, the demand forecasting module, if performing preprocessing, may decide to pause or adjust the preprocessing power. Upon receiving the message, the adaptive management module will know that it is not suitable to adjust the clamping force at present. Conversely, commands published by the intelligent management module will also be subscribed to by the access control module and used as input for its decision-making. The central scheduler within the central control unit 204 is responsible for arbitrating the execution timing and resources of all tasks, ensuring that high-priority tasks can be executed immediately and achieving cross-process timing coordination.

[0186] The vehicle domain controller 203 and its network include: a central gateway, body controller, vehicle controller, battery management system, charger, etc., which provide data services and control channels to the central control unit 204 via the vehicle CAN network. The vehicle energy management controller is also typically a node in this network.

[0187] The aforementioned user interface, as an input / output interface layer, consists of multiple physical and software interfaces. Its functions are implemented by corresponding controllers and interact with the central control unit 204 through standard protocols. The user interface is responsible for presenting the status and receiving commands, serving as a bridge between the user and the onboard power-assisted bicycle integrated system 200.

[0188] The sensor network includes an array of environmental sensing sensors for intelligent safety monitoring, as well as in-warehouse environmental sensors. Data from each node in the sensor network is fused by a dedicated sensing ECU, and the resulting environmental model is used by the scheduler as part of a comprehensive decision dataset.

[0189] The hardware of the charging and energy management system includes the vehicle-side OBC, bidirectional DC-DC converter, high and low voltage relay group, and the battery pack and battery management system on the electric bicycle side. It works in concert under the instructions of the central control unit 204 or the vehicle energy management controller to complete specific power conversion, transmission and safety management, and is the physical basis for implementing energy mutual assistance and collaborative charging.

[0190] The e-bike is compatible with the vehicle-mounted e-bike integration system 200. The e-bike features a standardized mechanical interface and charging interface compatible with the automatic storage mechanism 202, and its battery management system supports communication protocols with the vehicle's battery management system. Optionally, the e-bike can integrate a solar charging panel to charge the battery via a solar controller.

[0191] When a user sends a vehicle retrieval command via a mobile app, the command is transmitted to the central control unit 204 via the cloud and the vehicle network control unit. The central control unit 204 then verifies the vehicle's safety status (P gear, zero speed, etc.) through the vehicle network. Simultaneously, it queries the internal demand prediction module to confirm if there are any high-confidence predictions. Next, the central control unit 204's safety monitoring module assesses the access point environment risk as Level 0 using a sensor array. Then, the central control unit 204's scheduler generates the optimal motion control sequence based on current vehicle voltage, environmental information, etc., and executes the commands: opening the storage door, driving the slide to deliver the electric bicycle to the external pre-positioned location, releasing the lock, and prompting the user to retrieve the bicycle. Throughout this process, the various software modules exchange data in real time through a publish-subscribe mechanism, and the adaptive management module continuously calculates and maintains the optimal clamping force in the background based on real-time road condition data. After the user retrieves the electric bicycle, the sensors trigger, the slide automatically retracts, and the storage door closes. All steps are logged by the health management module. This process clearly demonstrates how the various hardware modules of the system, under the coordinated scheduling of the central control unit 204's intelligent software, implement the vehicle control method described above.

[0192] Exemplary control terminal In one exemplary embodiment of this application, a vehicle is also provided, see [link to example]. Figure 3 , Figure 3 This is a hardware architecture diagram of a vehicle provided in this application. The vehicle includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it performs the steps in the vehicle control method according to various embodiments of this application described in the above embodiments.

[0193] The vehicle includes a processor, memory, network interface, and input devices connected via a system bus. The vehicle's processor provides computing and control capabilities. The vehicle's memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The vehicle's network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it follows the steps of the vehicle control method according to various embodiments of this application described in the foregoing embodiments.

[0194] The processor may include the main processor, as well as baseband chips, modems, etc.

[0195] It is understood that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by the integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0196] It is understood that the memory in the embodiments of this application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Specifically, non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM). It should be noted that the memory in the devices and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0197] Input devices may include devices that receive data and information input by the user, such as keyboards, mice, cameras, scanners, light pens, voice input devices, touch screens, pedometers, or gravity sensors.

[0198] Output devices may include devices that allow information to be output to the user, such as displays, printers, speakers, etc.

[0199] The communication interface may include any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0200] The vehicle may also include a display component and a voice component. The display component may be an LCD screen or an e-ink screen. The input device of the vehicle may be a touch layer covering the display component, or it may be a button, trackball or touchpad set on the vehicle, or it may be an external keyboard, touchpad or mouse, etc.

[0201] Those skilled in the art can understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the vehicle to which the present application is applied. A specific vehicle may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0202] Exemplary computer program product and storage medium In addition to the methods and devices described above, the vehicle control method provided in this application may also be a computer program product, which includes computer program instructions that, when executed by a processor, cause the processor to perform the steps in the vehicle control method according to various embodiments of this application as described in the "Exemplary Methods" section above.

[0203] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0204] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of this application. These programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0205] In addition, this application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the vehicle control method according to various embodiments of this application as described in the "Exemplary Methods" section above.

[0206] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0207] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0208] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the solutions provided in this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A vehicle control method, characterized in that, An application to vehicles equipped with a vehicle-mounted electric bicycle, wherein the electric bicycle is detachably mounted in a closed storage compartment of the vehicle, comprising: Obtain access commands or system-generated access requests for the electric bicycle; In response to the access operation command or the access request, verify whether the vehicle is in a safe state that allows access; determine the user's potential use demand for the electric bicycle based on multi-source data, and perform at least one preprocessing operation according to the determination result; If the vehicle status verification is successful, the on-board automatic access mechanism is controlled to perform the assistive bicycle access action corresponding to the access operation command or access request. When the electric bicycle is in the vehicle-mounted storage state, the charging management strategy or physical retention strategy for the electric bicycle is adaptively adjusted according to dynamic conditions.

2. The vehicle control method according to claim 1, characterized in that, The step of determining potential user demand for the e-bike based on multi-source data and performing at least one preprocessing operation based on the determination result specifically includes: Establish and continuously update personalized travel habit profiles for users, which are generated based on users' historical travel data and associated with travel time, destination location, and whether or not the user uses the e-bike. Obtain real-time dynamic information related to the current trip, including the vehicle's navigation destination semantic information, real-time traffic flow data, parking resource information around the destination, and weather information; The real-time dynamic information is matched and calculated with the travel habit profile to generate a demand prediction confidence level; Specifically, the preprocessing operation is triggered only when the confidence level of the demand prediction exceeds the dynamically adjusted activation threshold; the preprocessing operation includes at least one of adjusting the internal environmental parameters of the closed storage compartment and preheating or precooling the battery of the electric bicycle.

3. The vehicle control method according to claim 1, characterized in that, The operation corresponding to the physical holding strategy is to dynamically adjust the clamping force of the locking mechanism in the closed storage compartment on the electric bicycle according to the bumpiness of the road surface, or to adjust the damping coefficient of the shock-absorbing element in the closed storage compartment. The operation corresponding to the charging management strategy is to control the electric bicycle battery to discharge to the vehicle's low-voltage electrical system when it is detected that the vehicle is in a parked state and its low-voltage battery power is lower than a first set value, while the electric bicycle battery power is higher than a second set value.

4. The vehicle control method according to claim 1, characterized in that, During the process of controlling the on-board automatic access mechanism to execute the electric bicycle access action corresponding to the access operation command or access request, the following steps are performed simultaneously: By fusing data from at least two different types of sensors located around the access port, a dynamic environment model outside the access port is constructed in real time. Identify potential risk elements in the dynamic environment model, including moving objects and low, stationary obstacles; Risk assessment is performed based on the type of identified risk factors, their trajectory, and the current motion state of the automated access mechanism. Based on different risk assessment levels, corresponding graded safety responses are executed, including: reducing movement speed and enhancing warnings, or immediately suspending movement and activating audible and visual alarms.

5. The vehicle control method according to claim 1, characterized in that, Performing the e-bike storage action includes: Control the opening of the closed storage compartment door and move the support part of the automatic storage mechanism to the external preparatory position; After the electric bicycle is placed in the support unit and locked, the support unit is controlled to retract into the closed storage compartment and the compartment door is closed. After the supporting part is retracted into place, the charging interface located in the enclosed storage compartment is automatically connected to the charging interface of the electric bicycle, and charging is started.

6. The vehicle control method according to claim 1, characterized in that, The method further includes: Monitor and record the storage status, battery health status, and data from each access and charge / discharge cycle of the electric bicycle. Based on the time series of the recorded access and charge / discharge data, the battery degradation trend is analyzed, and combined with the storage status, a maintenance suggestion report containing maintenance reminders or fault warnings is generated and pushed to the user terminal.

7. The vehicle control method according to claim 1, characterized in that, The method of controlling the on-board automatic access mechanism to perform the electric bicycle access action corresponding to the access operation command or the access request also includes: Real-time acquisition and fusion of environmental perception data, vehicle status data, and command priority information to form a comprehensive decision dataset; Based on the comprehensive decision dataset, the optimal motion control sequence of the automatic access mechanism is dynamically generated. The optimal motion control sequence includes the planning of the movement path and speed curve and its coordination with the execution timing of the preprocessing operation. Based on the optimal motion control sequence, the automatic access mechanism is controlled to perform access actions.

8. The vehicle control method according to claim 3, characterized in that, The operations corresponding to the physical preservation strategy also include: Continuously monitor the vehicle's dynamic driving parameters and predict potential severe impact risks based on parameter change trends; When the predicted risk exceeds a preset threshold, before the actual impact occurs, the locking mechanism of the closed storage compartment is actively controlled to enhance its force, and / or the electrical connection path between the electric bicycle battery and the vehicle charging system is cut off.

9. The vehicle control method according to claim 1, characterized in that, When the electric bicycle is in its onboard storage state, the collaborative charging strategy for the electric bicycle is adaptively adjusted according to dynamic conditions. The operation corresponding to the collaborative charging strategy is as follows: The battery of the electric bicycle is used as a controllable energy storage node in the vehicle's integrated energy management network. Based on the overall operating conditions of the vehicle, external power grid signals, user-preset scenario modes, and the priority of each power-consuming unit, the system collaboratively decides the charging and discharging timing and power of the electric bicycle battery in different scenarios such as vehicle driving, parking, or external power supply through a predetermined energy allocation strategy model.

10. A vehicle comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the vehicle control method as described in any one of claims 1-9.

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