A Collision Prediction Method and System for Mobile Charging Piles Based on Model Predictive Control

Abstract

This invention discloses a collision prediction method and system for mobile charging piles based on model predictive control, relating to the field of electric vehicle charging technology. The method includes the following steps: acquiring kinematic and dynamic parameters of the mobile charging pile during its movement, and establishing a dynamic model of the charging pile; constructing a model predictive control algorithm, setting an objective function and corresponding constraints; using the model predictive control algorithm to predict the charging pile's trajectory over a future period based on the dynamic model; acquiring surrounding environmental perception data, and predicting the collision risk between the charging pile and surrounding obstacles based on the trajectory and environmental perception data, obtaining collision risk analysis results; and adjusting the charging pile's movement strategy based on the collision risk analysis results. This invention can effectively analyze and avoid collision risks by predicting the charging pile's behavior over a future period, ensuring the safety and reliability of the charging process.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging technology, and in particular to a collision prediction method and system for mobile charging piles based on model predictive control. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Mobile charging stations are gaining increasing attention and application due to their flexibility and convenience. In urban environments, mobile charging stations need to provide charging services for electric vehicles within limited space. However, while providing convenient charging services, mobile charging stations also face the problem of potential harm to the surrounding environment and people during their movement. Due to space constraints and dynamically changing environmental conditions, charging stations are at risk of collisions with surrounding vehicles or obstacles during movement and repositioning. How to effectively analyze and prevent this risk has become an urgent problem to be solved.

[0004] Existing mobile charging stations mainly rely on simple obstacle avoidance systems, such as ultrasonic sensors. These are widely used due to their low cost and ease of installation. However, their detection range is limited, and they may not be accurate enough or react slowly in certain environments (such as windy or echoing environments), thus failing to effectively predict and avoid potential collision risks.

[0005] Chinese invention patent CN104002808A, published on August 28, 2014, describes an active collision avoidance automatic braking control system and its working method for automobiles. This technology uses PID control to monitor the vehicle's driving status and surrounding environment in real time. However, when faced with complex and dynamically changing traffic environments, it may not be able to effectively predict and respond to future changes.

[0006] Model predictive control (MPC) uses predictive models to forecast future system behavior and then controls based on these predictions. It better adapts to system variations and nonlinearities, offering high flexibility and the ability to simultaneously consider multiple control variables and constraints, such as limiting the maximum speed or acceleration of physical equipment. Compared to traditional PID control, it boasts superior predictive and optimization capabilities, providing better performance.

[0007] Currently, model predictive control (MPC) algorithms are widely used in automation and robotics, but their application in collision risk analysis of mobile charging stations is still insufficient.

[0008] Therefore, existing technologies lack accurate prediction of the movement trajectory of charging piles, making it impossible to achieve efficient and safe charging pile positioning in complex environments. Summary of the Invention

[0009] To address the shortcomings of existing technologies, the purpose of this invention is to provide a collision prediction method and system for mobile charging piles based on model predictive control. This method can effectively analyze and avoid collision risks by predicting the behavior of charging piles over a period of time, thereby ensuring the safety and reliability of the charging process.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0011] The first aspect of this invention provides a collision prediction method for mobile charging piles based on model predictive control, comprising the following steps:

[0012] Obtain the kinematic and dynamic parameters of the mobile charging pile during its movement, and establish a dynamic model of the charging pile;

[0013] Construct a model predictive control algorithm, and set the objective function and corresponding constraints;

[0014] The model predictive control algorithm is used to predict the movement trajectory of the charging pile over a future period of time based on the dynamic model of the charging pile;

[0015] Acquire surrounding environmental perception data, predict the collision risk between the charging pile and surrounding obstacles based on the motion trajectory and environmental perception data, and obtain the collision risk analysis results;

[0016] Adjust the charging pile's movement strategy based on the collision risk analysis results.

[0017] Furthermore, the specific steps for obtaining the kinematic and dynamic parameters of the mobile charging station during its movement are as follows:

[0018] The real-time location and speed of the charging station are obtained through GPS and IMU sensors;

[0019] Sensor data is fused using a Kalman filter.

[0020] Furthermore, after establishing the dynamic model of the charging pile, a nonlinear dynamic model is established based on the dynamic model of the charging pile to describe the motion behavior of the charging pile. The Taylor series expansion method is used to linearize the nonlinear dynamic model to adapt it to the MPC algorithm.

[0021] Furthermore, the objective function is defined as minimizing the distance to the obstacle, and the constraints include physical and safety limitations.

[0022] Furthermore, the specific steps for using model predictive control algorithms to predict the movement trajectory of the charging pile over a future period based on its dynamic model are as follows:

[0023] Based on preset control parameters, in each control cycle, the dynamic model of the charging pile is used to predict the movement trajectory of the charging pile in the future based on the current state.

[0024] Optimize the control input to satisfy the objective function and constraints;

[0025] The optimized control parameters are used as input for the next cycle.

[0026] Furthermore, the specific steps for predicting the collision risk between the charging pile and surrounding obstacles based on motion trajectory and environmental perception data are as follows:

[0027] Define a minimum safe distance; when the distance between the predicted trajectory and the obstacle is less than the minimum safe distance, a collision risk is considered to exist.

[0028] The collision probability is calculated by using statistical methods to estimate the likelihood of a collision based on the predicted trajectory and the location of obstacles.

[0029] Furthermore, the specific steps for adjusting the charging pile's movement strategy based on the collision risk analysis results are as follows:

[0030] Update the control input of the charging pile according to the set obstacle avoidance strategy;

[0031] The updated control input is used to control the charging station to move and avoid obstacles;

[0032] The control strategy is further adjusted based on the obstacle avoidance performance until the collision risk is less than the set threshold.

[0033] A second aspect of the present invention provides a collision prediction system for mobile charging piles based on model predictive control, comprising:

[0034] The data acquisition module is configured to acquire the kinematic and dynamic parameters of the mobile charging pile during its movement and to establish a dynamic model of the charging pile.

[0035] The algorithm configuration module is configured to build a model predictive control algorithm, setting the objective function and corresponding constraints.

[0036] The motion trajectory prediction module is configured to use a model predictive control algorithm to predict the motion trajectory of the charging pile over a future period of time based on the dynamic model of the charging pile.

[0037] The collision risk prediction module is configured to acquire surrounding environmental perception data, predict the collision risk between the charging pile and surrounding obstacles based on the motion trajectory and environmental data, and obtain the collision risk analysis results.

[0038] The motion strategy adjustment module is configured to adjust the motion strategy of the charging pile based on the collision risk analysis results.

[0039] A third aspect of the present invention provides a medium having a program stored thereon, which, when executed by a processor, implements the steps in the model predictive control-based mobile charging pile collision prediction method as described in the first aspect of the present invention.

[0040] A fourth aspect of the present invention provides an apparatus including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the model predictive control-based mobile charging pile collision prediction method as described in the first aspect of the present invention.

[0041] The above one or more technical solutions have the following beneficial effects:

[0042] This invention discloses a collision prediction method and system for mobile charging piles based on model predictive control. By predicting the movement trajectory of mobile charging piles in real time and analyzing potential collision risks, the possibility of collisions between charging piles and surrounding vehicles or obstacles can be significantly reduced. Reducing the occurrence of collision accidents can reduce the maintenance frequency and cost of charging piles, while also reducing service interruption time caused by accidents, thereby improving the availability of charging piles and the safety of the charging process.

[0043] The charging pile of the present invention can autonomously perceive and make decisions about the environment, reducing reliance on manual operation and enabling the charging pile to operate independently under a wider range of environmental conditions.

[0044] This invention employs an effective collision risk avoidance strategy, enabling charging stations to locate and charge electric vehicles more quickly, reducing time wasted searching for safe routes and thus improving charging efficiency. Because the charging stations can complete the charging task safely and quickly, users of the charging service can enjoy a more convenient and reliable charging experience.

[0045] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0046] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0047] Figure 1 This is a flowchart of the collision prediction method for mobile charging piles based on model predictive control in Embodiment 1 of the present invention.

[0048] Figure 2 This is a flowchart of the collision risk analysis and control process for mobile charging piles in Embodiment 1 of the present invention. Detailed Implementation

[0049] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0050] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0051] Example 1:

[0052] Embodiment 1 of the present invention provides a collision prediction method for mobile charging piles based on model predictive control, such as... Figure 1 As shown, a precise dynamic model of the mobile charging station is established, and a model predictive control (MPC) algorithm is designed to analyze the charging station's trajectory and surrounding environment in real time. By collecting and preprocessing data from multiple sensors, the MPC algorithm can predict the charging station's possible movement paths over a future period and analyze the potential collision risks between these paths and surrounding obstacles. Based on these analyses, the system automatically generates and executes avoidance strategies, such as adjusting speed or changing direction, to ensure the charging station safely navigates to the electric vehicle and begins charging, while avoiding any collisions. During execution, the system monitors the effectiveness of the avoidance actions and adjusts the control strategy through a feedback mechanism to optimize future predictions and avoidance actions, achieving high safety and efficiency throughout the charging process.

[0053] Specifically, the following steps are included:

[0054] Step 1: Obtain the kinematic and dynamic parameters of the mobile charging pile during its movement, and establish a dynamic model of the charging pile.

[0055] Step 1.1: Obtain the kinematic and dynamic parameters of the mobile charging station during its movement.

[0056] Step 1.1.1: Obtain the real-time location and speed of the charging station using GPS and IMU sensors.

[0057] Step 1.1.2: Use a Kalman filter to fuse sensor data to improve the accuracy of state estimation.

[0058] Step 1.2: Establish a dynamic model of the charging pile.

[0059] Constructing the state equations:

[0060] State equation: State equation in the x-direction: x(t+Δt)=x(t)+v x (t)Δt+1 / 2a x (t)(Δt) 2 .

[0061] Similarly, the state equation in the y-direction can be obtained as: y(t+Δt)=y(t)+v y (t)Δt+1 / 2a y (t)(Δt) 2。

[0062] Where x and y represent the position coordinates of the charging column on a two-dimensional plane, v x v y This indicates the speed of the charging station in the x and y directions; a x a y This indicates the acceleration of the charging station in the x-axis and y-axis directions.

[0063] Step 2: Construct the model predictive control algorithm, and set the objective function and corresponding constraints.

[0064] Step 2.1: After establishing the dynamic model of the charging pile, a nonlinear dynamic model is established based on the dynamic model of the charging pile to describe the motion behavior of the charging pile. The nonlinear dynamic model is linearized using the Taylor series expansion method to adapt to the MPC algorithm.

[0065] Step 2.2: Define the objective function as minimizing the distance to the obstacle, with constraints including physical and safety limitations.

[0066] In one specific implementation, the parameters are set as follows: prediction time range T = 10s, control period Δt = 0.1s, safety distance dsafe = 2m, and collision risk threshold Pthreshold = 0.1.

[0067] In this embodiment, the objective function can be defined not only as minimizing the distance to the obstacle, but also as minimizing energy consumption or changes in control input.

[0068] Step 3: Use model predictive control algorithms to predict the movement trajectory of the charging pile over a future period of time based on the dynamic model of the charging pile.

[0069] Step 3.1: Based on the preset control parameters, in each control cycle, the dynamic model of the charging pile is used to predict the movement trajectory of the charging pile in the future based on the current state and control input.

[0070] Step 3.2: Optimize the control input to satisfy the objective function and constraints.

[0071] Step 3.2.1: Define the optimization problem. This problem needs to minimize the objective function while satisfying system dynamics and constraints.

[0072] The optimization problem can be expressed as:

[0073] Where u is the control input sequence, x k Let be the state at time k, l be the cost function for each control step, V be the cost function for the final state, and N be the prediction time domain length.

[0074] Step 3.2.2: Optimize Control Input. In each control cycle, solve the optimization problem to find the optimal control input sequence. Add constraints to the optimization problem, including physical and safety limitations. Physical constraints include maximum speed and acceleration limits, while safety limitations include minimum safe distance. Dynamic constraints are also included, based on the charging pile's dynamic model, to ensure the control input remains within the system's dynamic capabilities.

[0075] Step 3.2.3: Input Execution. In each control cycle, only the first control input u0 in the optimization sequence is executed into the system.

[0076] Step 3.3: Apply the optimized control parameters as input for the next cycle.

[0077] Step 3.4: The system monitors the effectiveness of avoidance actions and adjusts the control strategy through a feedback mechanism to optimize future prediction and avoidance actions.

[0078] In one specific implementation, the actual movement of the charging pile and environmental changes are monitored in real time, and the model and control strategy are adjusted based on the deviation between the actual and predicted movements. If there is a significant deviation between the actual movement and the prediction, or if the environment changes, the objective function and constraints are adjusted.

[0079] Step 4: Acquire surrounding environmental perception data, predict the collision risk between the charging pile and surrounding obstacles based on the motion trajectory and environmental perception data, and obtain the collision risk analysis results, such as... Figure 2 As shown.

[0080] Step 4.1: Start: Start the collision risk analysis and MPC control system.

[0081] Step 4.2: Initialize the system and MPC parameters: Set initial conditions, including the initial position and speed of the charging pile. Prediction time range T = 10s, control period Δt = 0.1s.

[0082] Step 4.3: Obtain the current status of the charging pile: Charging pile status data fusion refers to dynamically obtaining the real-time operating status of the charging station, such as obtaining the real-time position (x, y) and speed (v) of the charging pile through sensors.x v y It also includes multiple sets of real-time usage information for multiple charging piles in the charging station, and dynamically obtains the user's scheduled charging time sequence, including the user's scheduled charging information.

[0083] Step 4.4: Acquire Environmental Perception Data: Use sensors such as LiDAR and cameras to collect data on the surrounding environment, including the location of obstacles (o x o y ) and speed (o vx o vy Then, by extracting the decision-making characteristics of drivers and passengers through sensor data, we can analyze the charging station selection preferences and charging behavior characteristics of public charging station users.

[0084] In this embodiment, obstacles can be categorized into static and dynamic obstacles. Static obstacles can be read from a high-precision map or sensed in real time by sensors, while dynamic obstacles require analysis of data obtained by sensors over multiple periods to obtain their motion information. Obstacles encompass both static and dynamic obstacles, such as other vehicles, pedestrians, and buildings, and can be detected and tracked using sensor data.

[0085] The obstacle information was obtained using existing technologies, which will not be elaborated here.

[0086] Step 4.5: MPC algorithm predicts future trajectory: Using the MPC algorithm, the movement trajectory of the charging pile in the next 10 seconds is predicted based on the current state and environment, and the prediction is updated every 0.1 seconds.

[0087] Step 4.6: Data Fusion and Dynamic Model Establishment: Fusion of sensor data and charging pile status data to establish a dynamic model of the charging pile and the environment.

[0088] Step 4.6.1: Establishing the Dynamic Model Framework. This is a charging pile scheduling system based on dynamic data fusion. The system communicates with multiple data sources, including charging stations and users. The model construction process must reflect movement relationships, specifically the movement relationships between charging piles and obstacles, and between charging piles and user vehicles. During model building, user behavior analysis, charging load prediction, and guidance can be used to improve system performance and construct a more comprehensive dynamic model of charging piles and the environment. For example, based on mobile phone signal data and charging station data, typical travel patterns of vehicle users and their correlation with charging load can be identified and incorporated into the model training process as one of the influencing factors.

[0089] Step 4.6.2: Model Parameter Solving. In the model parameter solving process, the candidate charging stations are reconstructed starting from the user's location 10 minutes before charging. Considering the differences in the dimensions of various influencing factors, min-max normalization is performed on each factor, and the maximum likelihood method is used for parameter fitting. Influencing factors with a confidence level less than 0.95 are gradually eliminated during the solution process. After solving the model parameters, the optimal dynamic model parameters for the charging pile and the environment are obtained.

[0090] Step 4.7: Assess the collision risk of the predicted trajectory: Based on the dynamic model of the charging station and the environment, assess the potential collision risk between the charging station and surrounding obstacles. Safe distance dsafe = 2m, collision probability Pcollision.

[0091] Among them, the risk assessment index is defined as follows: the minimum safe distance dsafe is defined, and when the distance between the predicted trajectory and the obstacle is less than this value, it is considered that there is a collision risk; the collision probability P is calculated, and the possibility of collision is estimated by statistical methods based on the predicted trajectory and the position of the obstacle.

[0092] Risk assessment process: For each predicted trajectory, calculate the minimum distance to surrounding obstacles; if the minimum distance is less than the safe distance, calculate the collision probability and compare it with a threshold; if the collision probability exceeds the threshold, trigger an emergency obstacle avoidance strategy.

[0093] Step 4.8: Risk assessment results: If the collision probability Pcollision > 0.1, the risk is high and the MPC objective function needs to be adjusted to reduce the risk; if the collision probability Pcollision ≤ 0.1, the risk is acceptable and the current MPC strategy continues to be executed.

[0094] Step 5: Adjust the charging pile's movement strategy based on the collision risk analysis results.

[0095] Step 5.1: Update the control input of the charging pile according to the set obstacle avoidance strategy.

[0096] In this embodiment, a safe path to avoid the obstacle is planned based on the position and speed information of the obstacle, as well as the current position and speed of the charging pile.

[0097] Specifically, the adjustment and updating of control inputs mainly include setting control parameters, model predictive control, real-time feedback, implementation of control inputs, and monitoring and iteration.

[0098] First, control parameters, such as maximum acceleration, maximum deceleration, and maximum steering angle, are set according to the obstacle avoidance strategy. Then, the MPC algorithm is used to optimize the control input based on the current state and predicted obstacle movement to achieve obstacle avoidance and path tracking. Environmental information is fed back in real time by sensors, and the control input is dynamically adjusted to cope with sudden obstacle appearances or path changes. The optimized control input is then implemented in the charging pile's control system, such as adjusting the motor's power output and the steering angle of the steering system. In the next control cycle, the environment and charging pile status are monitored again, and the process of adjusting and updating the obstacle avoidance strategy and control input is repeated.

[0099] In this embodiment, the risk is reduced by adjusting the MPC objective function: the objective function of the MPC algorithm is adjusted to increase the safety weight, thereby reducing the collision risk.

[0100] Step 5.2: Apply the updated control input to control the charging pile to move and achieve obstacle avoidance.

[0101] In this embodiment, control input is applied to the system: the control input calculated by the MPC algorithm, such as acceleration (α) x ,a y This technology is applied to the control system of charging piles.

[0102] Step 5.3: Further adjust the control strategy based on the obstacle avoidance effect until the collision risk is less than the set threshold.

[0103] Step 5.3.1: Monitoring and Adjustment: If the obstacle avoidance effect is not ideal, further adjust the control strategy until the risk is acceptable. Update the status every Δt = 0.1s, continuously monitoring the charging pile and environmental status.

[0104] Step 5.4: Monitor system status and environment: Continuously monitor the new status of the charging pile and environmental changes to provide data support for the next control cycle.

[0105] Step 5.5: Continue with the current MPC strategy or re-predict: If the risk is acceptable, continue with the current MPC strategy. If the risk is high, re-predict the trajectory and re-assess the collision risk.

[0106] Example 2:

[0107] Embodiment 2 of the present invention provides a mobile charging pile collision prediction system based on model predictive control, comprising:

[0108] The data acquisition module is configured to acquire the kinematic and dynamic parameters of the mobile charging pile during its movement and to establish a dynamic model of the charging pile.

[0109] The algorithm configuration module is configured to build a model predictive control algorithm, setting the objective function and corresponding constraints.

[0110] The motion trajectory prediction module is configured to use a model predictive control algorithm to predict the motion trajectory of the charging pile over a future period of time based on the dynamic model of the charging pile.

[0111] The collision risk prediction module is configured to acquire surrounding environmental perception data, predict the collision risk between the charging pile and surrounding obstacles based on the motion trajectory and environmental data, and obtain the collision risk analysis results.

[0112] The motion strategy adjustment module is configured to adjust the motion strategy of the charging pile based on the collision risk analysis results.

[0113] Example 3:

[0114] Embodiment 3 of the present invention provides a medium on which a program is stored. When the program is executed by a processor, it implements the steps in the collision prediction method for mobile charging piles based on model predictive control as described in Embodiment 1 of the present invention.

[0115] Example 4:

[0116] Embodiment 4 of the present invention provides a device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the collision prediction method for mobile charging piles based on model predictive control as described in Embodiment 1 of the present invention.

[0117] The steps and methods involved in Examples 2, 3 and 4 above correspond to those in Example 1. For specific implementation details, please refer to the relevant description section of Example 1.

[0118] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0119] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A collision prediction method for mobile charging piles based on model predictive control, characterized in that, Includes the following steps: Obtain the kinematic and dynamic parameters of the mobile charging pile during its movement, and establish a dynamic model of the charging pile; Construct a model predictive control algorithm, and set the objective function and corresponding constraints; The model predictive control algorithm is used to predict the movement trajectory of the charging pile within a certain period of time based on the dynamic model of the charging pile. The specific steps are as follows: according to the preset control parameters, in each control cycle, the dynamic model of the charging pile is used to predict the movement trajectory of the charging pile within a certain period of time based on the current state; the control input is optimized to meet the objective function and constraints; and the optimized control parameters are used as the input for the next cycle. Acquire surrounding environmental perception data, predict the collision risk between the charging pile and surrounding obstacles based on the motion trajectory and environmental perception data, and obtain the collision risk analysis results; The movement strategy of the charging pile is adjusted based on the collision risk analysis results. The specific steps are as follows: update the control input of the charging pile according to the set obstacle avoidance strategy; apply the updated control input to control the charging pile to move and achieve obstacle avoidance; further adjust the control strategy based on the obstacle avoidance effect until the collision risk is less than the set threshold.

2. The collision prediction method for mobile charging piles based on model predictive control as described in claim 1, characterized in that, The specific steps for obtaining the kinematic and dynamic parameters of a mobile charging station during its movement are as follows: The real-time location and speed of the charging station are obtained through GPS and IMU sensors; Sensor data is fused using a Kalman filter.

3. The collision prediction method for mobile charging piles based on model predictive control as described in claim 1, characterized in that, After establishing the dynamic model of the charging pile, a nonlinear dynamic model is established based on the dynamic model of the charging pile to describe the motion behavior of the charging pile. The Taylor series expansion method is used to linearize the nonlinear dynamic model to adapt it to the MPC algorithm.

4. The collision prediction method for mobile charging piles based on model predictive control as described in claim 1, characterized in that, The objective function is defined as minimizing the distance to the obstacle, and the constraints include physical and safety limitations.

5. The collision prediction method for mobile charging piles based on model predictive control as described in claim 1, characterized in that, The specific steps for predicting the collision risk between charging piles and surrounding obstacles based on motion trajectory and environmental perception data are as follows: Define a minimum safe distance; when the distance between the predicted trajectory and the obstacle is less than the minimum safe distance, a collision risk is considered to exist. The collision probability is calculated by using statistical methods to estimate the likelihood of a collision based on the predicted trajectory and the location of obstacles.

6. A collision prediction system for mobile charging piles based on model predictive control, characterized in that, include: The data acquisition module is configured to acquire the kinematic and dynamic parameters of the mobile charging pile during its movement and to establish a dynamic model of the charging pile. The algorithm configuration module is configured to build a model predictive control algorithm, setting the objective function and corresponding constraints. The motion trajectory prediction module is configured to use a model predictive control algorithm to predict the motion trajectory of the charging pile within a future period based on the dynamic model of the charging pile. The specific steps are as follows: according to the preset control parameters, in each control cycle, the dynamic model of the charging pile is used to predict the motion trajectory of the charging pile within a future period based on the current state; the control input is optimized to meet the objective function and constraints; and the optimized control parameters are applied as the input for the next cycle. The collision risk prediction module is configured to acquire surrounding environmental perception data, predict the collision risk between the charging pile and surrounding obstacles based on the motion trajectory and environmental data, and obtain the collision risk analysis results. The motion strategy adjustment module is configured to adjust the motion strategy of the charging pile based on the collision risk analysis results. The specific steps are as follows: update the control input of the charging pile according to the set obstacle avoidance strategy; apply the updated control input to control the charging pile to move and achieve obstacle avoidance; further adjust the control strategy based on the obstacle avoidance effect until the collision risk is less than the set threshold.

7. A computer-readable storage medium, characterized in that, It stores multiple instructions, which are adapted to be loaded and executed by the processor of the terminal device as described in any one of claims 1-5, for the mobile charging pile collision prediction method based on model predictive control.

8. A terminal device, characterized in that, The device includes a processor and a computer-readable storage medium, wherein the processor implements various instructions; and the computer-readable storage medium stores multiple instructions adapted to be loaded by the processor and executed by the processor for the collision prediction method for mobile charging piles based on model predictive control as described in any one of claims 1-5.

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