Mobile charging device driving control method and system for electric vehicles

Abstract

The application discloses a mobile charging equipment driving control method and system for an electric vehicle, and relates to the technical field of mobile charging. The method comprises the following steps: receiving a first driving instruction by a receiving controller; transmitting the first driving instruction to a motor controller for instruction analysis, obtaining a first driving control parameter; performing source identification on the first driving instruction, obtaining a first source identification result; when the first source identification result is from a domain controller, performing dynamic optimization on the first driving control parameter, and obtaining a second driving control parameter as a second driving instruction; transmitting the second driving instruction to the motor controller for instruction analysis, obtaining the second driving control parameter, and transmitting the second driving control parameter to a driving motor system to drive the motor. The technical problem of insufficient intelligentization of the driving control of the mobile charging equipment of the electric vehicle in the prior art is solved, and the technical effect of improving the intelligentization of the driving control is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile charging, in particular to a mobile charging device driving control method and system for electric vehicles. BACKGROUND

[0002] Mobile charging piles can be quickly deployed to any location according to actual needs, whether in urban centers, suburbs or remote areas, and can provide timely charging services for electric vehicles. This flexibility enables mobile charging piles to adapt to various scenarios, especially in old neighborhoods, temporary parking lots, service areas and other places where fixed charging piles are difficult to cover. Mobile charging piles can easily move to the place where charging is needed. However, there are still some problems to be solved in the driving control of the mobile charging device. The existing driving control method is mostly simple and fixed, lacking the ability to identify and process different instruction sources, and it is difficult to dynamically optimize and adjust the driving parameters according to complex and variable environmental information. This results in the mobile charging device, during driving, may not be able to accurately follow the planned path, and when encountering obstacles or complex road conditions, it cannot make reasonable driving adjustments in a timely manner, thereby affecting its safety and stability during driving, and limiting its application effect in various complex scenarios. SUMMARY

[0003] The present application provides a mobile charging device driving control method and system for electric vehicles, which solves the technical problem of insufficient intelligence of electric vehicle mobile charging device driving control in the prior art.

[0004] In a first aspect, the present application provides a mobile charging device driving control method for electric vehicles, the method comprising:

[0005] The first driving instruction is received by the receiving controller; the first driving instruction is transmitted to the motor controller for instruction analysis to obtain the first driving control parameter; the source of the first driving instruction is identified to obtain the first source identification result; when the first source identification result is from the domain controller, the planned path of the intelligent positioning and navigation system is called, the first driving control parameter is dynamically optimized in the domain controller combined with the main laser radar and ultrasonic radar, and the obtained second driving control parameter is taken as the second driving instruction; the second driving instruction is transmitted to the motor controller for instruction analysis to obtain the second driving control parameter, and the second driving control parameter is transmitted to the driving motor system to drive the motor.

[0006] In a second aspect, the present application provides a mobile charging device driving control system for electric vehicles, the system comprising:

[0007] The instruction receiving module is used for receiving a first driving instruction through a receiving controller; the instruction analysis module is used for transmitting the first driving instruction to a motor controller for instruction analysis, to obtain a first driving control parameter; the identification module is used for source identification of the first driving instruction, to obtain a first source identification result; the optimization module is used for, when the first source identification result is from a domain controller, calling a planned path of an intelligent positioning navigation system, combining the main laser radar and the ultrasonic radar to dynamically optimize the first driving control parameter in the domain controller, and taking a second driving control parameter obtained as a second driving instruction; and the driving module is used for transmitting the second driving instruction to the motor controller for instruction analysis, to obtain a second driving control parameter, and transmitting the second driving control parameter to a driving motor system to drive a motor.

[0008] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The first driving instruction is received through a receiving controller. The first driving instruction is transmitted to a motor controller for instruction analysis, to obtain a first driving control parameter. The first driving instruction is subjected to source identification, to obtain a first source identification result. When the first source identification result is from a domain controller, a planned path of an intelligent positioning navigation system is called, the main laser radar and the ultrasonic radar are combined to dynamically optimize the first driving control parameter in the domain controller, and a second driving control parameter obtained is taken as a second driving instruction. The second driving instruction is transmitted to the motor controller for instruction analysis, to obtain a second driving control parameter, and the second driving control parameter is transmitted to a driving motor system to drive a motor. The technical problem of insufficient intelligentization of driving control of a mobile charging device for an electric vehicle in the prior art is solved. Intelligent path planning and dynamic optimization of driving control parameters are used, to achieve the technical effect of improving intelligentization of driving control. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0011] Figure 1 A flowchart of a driving control method for a mobile charging device for an electric vehicle is provided for the embodiments of the present application.

[0012] Figure 2 A structural schematic diagram of a driving control system for a mobile charging device for an electric vehicle is provided for the embodiments of the present application.

[0013] Explanation of reference signs: instruction receiving module 11, instruction analyzing module 12, identification module 13, optimization module 14, driving module 15. DETAILED DESCRIPTION

[0014] The present application provides a mobile charging device driving control method and system for electric vehicles, which solves the technical problem of insufficient intelligence of electric vehicle mobile charging device driving control in the prior art.

[0015] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0016] It should be noted that the terms “comprising” and “having” are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to the process, method, product or device.

[0017] Embodiment one, as shown in the present application provides a mobile charging device driving control method for electric vehicles, which is applied to a driving control system of a charging device, the driving control system comprising a domain controller, an intelligent positioning and navigation system, a receiving controller, a motor controller and a driving motor system, wherein the method comprises: Figure 1

[0018] Receiving the first driving instruction through the receiving controller.

[0019] The receiving controller is part of the electric vehicle mobile charging device driving control system, responsible for receiving and processing driving instructions from different sources. The first driving instruction is received through the receiving controller, which is a driving control request from an external system or a user.

[0020] Transmitting the first driving instruction to the motor controller for instruction analysis to obtain the first driving control parameter.

[0021] When the receiving controller receives the first driving instruction, the instruction is transmitted to the motor controller through a communication interface (such as a CAN bus). After the motor controller receives the first driving instruction, the instruction is analyzed by the internal instruction analysis module to extract the control parameters related to the operation of the motor, including the speed, torque and acceleration of the motor, which guide the motor to operate according to the predetermined behavior. ​

[0022] The first driving control parameter is obtained by analyzing the motor controller, which will be used for the actual driving control of the motor. The first driving control parameter includes the speed, output power, operating state and other information of the motor, which provides the basis for subsequent driving actions.

[0023] The first driving instruction is identified by the source to obtain the first source identification result.

[0024] By analyzing the relevant information of the first driving instruction, such as the sending source address, identifier or communication protocol of the control signal, it is determined which system module or external device the instruction comes from.

[0025] Specifically, the receiving controller compares and verifies the specific information in the instruction (such as communication protocol identifier, source device ID, transmission channel, etc.), judges the source of the instruction, and obtains the first source identification result, which can be different sources such as domain controller, Bluetooth module, wireless communication device, etc.

[0026] Further, when the first source identification result is from the Bluetooth transmission module, the first driving control parameter is transmitted to the driving motor system to drive the motor.

[0027] When the first source identification result is from the Bluetooth transmission module, the system confirms that the instruction is sent by the Bluetooth device (such as a smart phone, a remote control device, etc.) through Bluetooth wireless communication. In this case, the receiving controller will transmit the received first driving control parameter to the driving motor system according to the identified source of the Bluetooth signal. After receiving the first driving control parameter, the driving motor system will accurately drive the motor according to these parameters. The driving motor system adjusts the operating state of the motor according to the instruction, thereby realizing the driving control of the electric vehicle mobile charging device and ensuring that the device moves according to the predetermined requirements.

[0028] Further, it also includes:

[0029] The historical driving log sequence is retrieved in a preset backtracking window; the driving deviation factor-deviation direction set is obtained based on the historical driving log sequence; the target driving deviation factor-deviation direction is determined by screening and identifying the driving deviation factor-deviation direction set; and the first driving control parameter is corrected based on the target driving deviation factor-deviation direction.

[0030] Within a preset backtracking window, the system analyzes the device's past performance by retrieving historical drive log sequences. The size of the preset backtracking window can be defined based on a predetermined time range or specific events (such as charging operation cycles or device malfunctions). The historical drive log sequences contain various drive parameters and control data recorded during past operations of the electric vehicle or charging equipment. Based on these historical drive log sequences, the system obtains a set of drive deviation factors and deviation directions. The drive deviation factor refers to the difference between the actual operation and the expected operation, i.e., the ratio of the deviation in distance traveled to the distance traveled. The deviation in distance traveled refers to the deviation between the motor's trajectory and the ideal path during the actual driving process of the electric vehicle or mobile charging equipment. The distance traveled is the actual distance the motor traveled. The deviation direction is the deviation between the direction of movement and the preset direction.

[0031] By screening and identifying the set of drive deviation factors and their corresponding directions, the system determines the target drive deviation factor and its direction. In other words, the system filters based on the degree of influence of each deviation factor on the drive system performance, identifying the most influential drive deviation factor and its corresponding direction. Based on the target drive deviation factor and its direction, the system corrects the first drive control parameters. By identifying and analyzing the target drive deviation factor, the system can adjust the drive control parameters, thereby eliminating or reducing deviations and improving the drive accuracy of electric vehicles or charging equipment.

[0032] Furthermore, the process of filtering and identifying the set of driving deviation factors and deviation directions to determine the target driving deviation factor and deviation direction includes:

[0033] The driving deviation factors-deviation directions are aggregated into similar sets to obtain K clustering driving deviation factors-deviation directions sets, where K is a positive integer. The data volume in each of the K clustering driving deviation factors-deviation directions sets is counted, and sets with data volumes below a preset data volume threshold are removed to obtain M filtered clustering driving deviation factors-deviation directions sets. Each filtered clustering driving deviation factors-deviation directions set includes an identification weight, where M is a positive integer less than or equal to K. The centers of the M filtered clustering driving deviation factors-deviation directions sets are identified through traversal, and M center filtered clustering driving deviation factors-deviation directions are determined. These centers are then weighted and calculated using their corresponding identification weights to obtain the target driving deviation factor-deviation direction.

[0034] The system aggregates the same type of driving bias factor and bias direction, i.e., bias factors and bias directions with similar characteristics or influencing factors are classified into the same category. Through a clustering algorithm (such as K-means clustering), the system can automatically divide the set of bias factors and bias directions into multiple clusters, each cluster containing data with similar bias characteristics. After aggregation, the system obtains K sets of clustered driving bias factors and bias directions, where K is a positive integer representing the number of clusters.

[0035] By counting the amount of data in each cluster set, the representativeness and effectiveness of each cluster are evaluated. If some clusters contain a small amount of data, which is lower than the preset data amount threshold, it is considered that the bias factors and bias directions of the cluster do not have enough influence, which may be caused by data errors or other abnormal situations. Therefore, the system eliminates these low-data-amount clusters and retains clusters with sufficient data amount and representativeness. Finally, the system obtains M sets of filtered cluster driving bias factors and bias directions, where M is a positive integer less than or equal to K.

[0036] On this basis, the system iterates through the M sets of filtered cluster driving bias factors and bias directions to identify the center. Specifically, the system identifies the center bias factor and bias direction of each cluster, which represent the most representative or typical bias characteristics within the cluster. Through center identification, the system can find the bias factors and bias directions that have the greatest impact on device operation. Finally, the system performs weighted calculation on the M sets of center filtered cluster driving bias factors and bias directions in combination with the identified weights of each cluster. Each cluster is assigned a weight according to its influence and data amount, and the cluster with a larger weight has a greater impact on the final result. Optionally, the data amount of each set is divided by the total data amount after filtering to obtain the identification weight. Through weighted calculation, the system finally obtains the target driving bias factor and bias direction, which are the basis for the system to correct the control parameters.

[0037] Further, mean shift identification is performed on each of the M sets of filtered cluster driving bias factors and bias directions to obtain the M sets of center filtered cluster driving bias factors and bias directions.

[0038] Preferably, the system takes each cluster of filtered driving bias factors and bias directions as input data for mean shift identification. Mean shift is an unsupervised clustering method that iteratively calculates the local mean of data points until they converge to areas of high data density, ultimately identifying the center of the cluster. In this process, the system first selects the initial position of each data point and calculates the mean of the area around it. In each iteration, the system adjusts the position of the data point based on the mean of the current area, gradually moving towards areas of high density in the data set. This process is repeated until the mean of all data points stabilizes and no longer changes significantly in position.

[0039] Through the mean shift method, the system can effectively extract the central features from each filtered cluster, and these central features are the most important driving bias factors and bias directions within the cluster. After processing by the mean shift algorithm, the system ultimately obtains M central filtered driving bias factors and bias directions. The driving bias factors and bias directions of these central filtered clusters represent the core characteristics of their respective clusters and can fully reflect the most critical bias trends in each cluster.

[0040] When the first source identification result is from the domain controller, the planned path of the intelligent positioning and navigation system is invoked, and the first driving control parameter is dynamically optimized within the domain controller in combination with the main laser radar and ultrasonic radar, and the obtained second driving control parameter is taken as the second driving instruction.

[0041] When the receiving controller confirms that the first source identification result is from the domain controller, the domain controller will invoke the planned path in the intelligent positioning and navigation system according to the target position and task requirements of the mobile charging device of the electric vehicle.

[0042] The system scans and perceives the environment around the charging device in combination with the main laser radar and ultrasonic radar. The main laser radar scans the surrounding obstacles, terrain changes, and potential hazards through laser beams and generates high-precision three-dimensional point cloud data; the ultrasonic radar detects the distance of obstacles at close range to help the system identify the position and motion state of obstacles in real time.

[0043] After obtaining the path planning information and real-time environmental data, the domain controller will optimize and adjust the first driving control parameter according to the current dynamic environmental information, including adjusting the motor speed, acceleration, motion trajectory, and other parameters to adapt to the changing external conditions, such as obstacle avoidance, path change, or device load change. The second driving control parameter after dynamic optimization is transmitted to the motor controller as the second driving instruction.

[0044] Further, the domain controller, the intelligent positioning and navigation system, the receiving controller, the motor controller and the driving motor system are connected to each other through the CAN bus.

[0045] In the embodiments of the present application, the domain controller, the intelligent positioning and navigation system, the receiving controller, the motor controller and the driving motor system are connected to each other and communicate through the CAN bus. The CAN bus has multi-host support capability, which can enable multiple devices to work in parallel in the same network, and each device can actively send or receive data.

[0046] Through the CAN bus, each control module can efficiently share information and perform real-time control synchronously. For example, after the receiving controller receives external driving instructions, it transmits these instructions to the motor controller through the CAN bus for processing. After the motor controller analyzes the instructions, it generates specific driving control parameters and sends them to the driving motor system through the CAN bus, thereby achieving the driving of the motor. At the same time, after the domain controller obtains path planning data from the intelligent positioning and navigation system, it transmits these data to the receiving controller and the motor controller through the CAN bus, ensuring that the charging device can move accurately according to the planned path.

[0047] In the process of path planning and dynamic optimization, the domain controller communicates with the intelligent positioning and navigation system through the CAN bus to obtain current path information and perform dynamic updates. When the environment changes (such as obstacle detection or path deviation), the feedback data of the main laser radar and ultrasonic radar are transmitted to the domain controller through the CAN bus, and the domain controller optimizes the path in real time based on these data and transmits the updated driving control parameters to the motor controller through the CAN bus. The motor controller then adjusts the operating state of the driving motor to ensure that the charging device can adapt to the changing environment.

[0048] Further, when the first source identification result is from the domain controller, the planned path of the intelligent positioning and navigation system is retrieved, and the first driving control parameters are dynamically optimized in the domain controller based on the main laser radar and ultrasonic radar, and the obtained second driving control parameters are taken as the second driving instructions, including:

[0049] Based on the main laser radar and the ultrasonic radar, continuous environmental scanning is performed to obtain dynamic environmental information and static environmental information; real-time positioning is performed using the GPS loaded on the charging device to determine the real-time position; the planned path is dynamically adjusted based on the dynamic environmental information and static environmental information and the real-time position to obtain a first adjusted planned path; the first driving control parameters are optimized based on the first adjusted planned path using the parameter optimizer arranged in the domain controller to obtain the second driving control parameters.

[0050] The system utilizes a main laser radar and an ultrasonic radar to perform real-time scanning of the environment around the electric vehicle mobile charging device. The main laser radar scans the surrounding obstacles, ground undulations, and other environmental changes through laser beams, generating a high-precision three-dimensional point cloud map and providing detailed spatial information. The ultrasonic radar scans the environment at a closer distance, providing the distance and motion state of obstacles. Through the cooperation of the main laser radar and the ultrasonic radar, the system can obtain dynamic environmental information (such as moving obstacles, road condition changes) and static environmental information (such as road boundaries, fixed obstacle positions), providing data support for subsequent path optimization.

[0051] The system utilizes a GPS mounted on the charging device for real-time positioning. Through the GPS module, the system can accurately determine the current position of the charging device in the actual environment. The system combines the dynamic environmental information, static environmental information, and real-time position to dynamically adjust the planned path and generate a first adjusted planned path. By combining the data provided by multiple sensors, the system can optimize the path in real time, avoid obstacles, and improve travel efficiency. Finally, the system utilizes a parameter optimizer arranged in the domain controller to optimize the first driving control parameters according to the first adjusted planned path, obtaining the second driving control parameters.

[0052] When the intelligent positioning and navigation system generates a first adjusted planned path based on real-time environment and path adjustment, the system transmits this path information to the parameter optimizer in the domain controller. After receiving the path data, the parameter optimizer combines the current first driving control parameters (such as motor speed, acceleration, steering angle, etc.) for dynamic adjustment, ensuring that the electric vehicle mobile charging device can travel efficiently and safely on the updated path. The optimization includes adjusting the driving parameters of the motor according to environmental changes (such as obstacles, ground undulations, etc.), thereby minimizing path deviation, improving energy utilization efficiency, and ensuring smoothness and safety of travel. The parameter optimizer iteratively optimizes to obtain the optimal control parameters that meet the current path requirements and environmental conditions, and generates the second driving control parameters.

[0053] Further, based on the continuous environmental scanning by the main laser radar and the ultrasonic radar, dynamic environmental information and static environmental information are obtained, including:

[0054] The main laser radar emits laser beams and scans the surrounding environment in a preset scanning period through rotational scanning, collecting a sequence of periodic laser signal sets. The sequence of periodic laser signal sets is traversed to generate a three-dimensional point cloud map, obtaining a sequence of environmental three-dimensional point cloud maps. Dynamic iterative identification is performed on the sequence of environmental three-dimensional point cloud maps to determine the moving direction set and the moving speed set of the moving target set, and the moving direction set and the moving speed set are taken as dynamic environmental information.

[0055] Specifically, the main laser radar emits a laser beam and scans the surrounding environment in a preset scanning period through rotational scanning. The main laser radar obtains three-dimensional spatial information of the environment by emitting a laser beam and receiving laser signals reflected from surrounding objects. During scanning, the laser radar periodically scans the surrounding environment at a set period and continuously collects a sequence of feedback laser signal sets. Each round of scanning generates a set of laser signals, which include the reflection characteristics of objects in the environment. Then, the system traverses the sequence of periodic laser signal sets, processes each round of laser signals, and generates a three-dimensional point cloud map. The point cloud map is a three-dimensional spatial data set obtained by laser radar scanning, representing the positions, shapes, and distributions of objects in the surrounding environment. The system serializes these point cloud data to generate a continuous sequence of three-dimensional point cloud maps of the environment, forming a detailed three-dimensional model of the surrounding environment. Then, the system dynamically iteratively identifies the sequence of three-dimensional point cloud maps of the environment. In this process, the system continuously iteratively updates and identifies object features in the point cloud map, identifies moving targets, and analyzes their movement patterns based on changes in the point cloud map to extract the moving direction set and the moving speed set of the moving target set, thereby obtaining information about the dynamic environment.

[0056] Further, the dynamic environment information and the static environment information are taken as interference factors, and the position of the real-time position in the planning path is taken as an adjustment point. The path adjuster adjusts the planning path according to the adjustment point and the interference factors to obtain a first adjusted planning path.

[0057] The system continuously obtains dynamic environment information from the main laser radar and the ultrasonic radar, as well as static environment information (such as fixed obstacles, road structures, etc.), and takes these information as potential interference factors. The dynamic environment information includes the speed and direction of moving obstacles, and the static environment information involves the geometry of the road, the position of the fixed obstacles, etc.

[0058] The system obtains the real-time position of the electric vehicle mobile charging device, and takes the real-time position as an adjustment point. The adjustment point is the result of comparing the current position of the device with the position on the original planning path, reflecting the actual coordinates of the device in the current environment. Using this real-time position information, the system can determine the deviation point in the current path and then adjust the path.

[0059] The system adjusts the planned path using a path adjuster, which is an integrated module that optimizes the original planned path based on real-time location and interference factors. The path adjuster automatically corrects or adjusts the driving path of the device by analyzing interference factors such as moving obstacles in the dynamic environment, static obstacles, traffic conditions, etc., combined with the information of the adjustment points, to avoid obstacles and collisions, and ensure the optimality of driving. Based on the adjustment result of the path adjuster, the system obtains the first adjusted planning path.

[0060] The second driving instruction is transmitted to the motor controller for instruction analysis, and the second driving control parameter is obtained, and the second driving control parameter is transmitted to the driving motor system to drive the motor.

[0061] After path adjustment and environmental optimization, the system generates a second driving instruction containing second driving control parameters, including motor speed, acceleration, torque, and other control requirements. The second driving instruction is transmitted to the motor controller through a communication protocol such as CAN bus. After receiving the second driving instruction, the motor controller analyzes it and extracts the control parameters, and converts them into signals required for motor driving. These signals are processed in the motor controller to generate corresponding second driving control parameters such as motor speed and load adjustment, to ensure accurate control of the motor according to the optimized path and environment. Then, the motor controller transmits the optimized second driving control parameters to the driving motor system, which receives these parameters and adjusts the motor's operating state, such as adjusting motor speed and torque output, to achieve precise driving of the electric vehicle mobile charging device and ensure smooth progress along the optimized and adjusted path.

[0062] In summary, the embodiments of the present application have at least the following technical effects:

[0063] The first driving instruction is received by the receiving controller. The first driving instruction is transmitted to the motor controller for instruction analysis, and the first driving control parameter is obtained. The source of the first driving instruction is identified, and the first source identification result is obtained. When the first source identification result is from the domain controller, the planned path of the intelligent positioning and navigation system is retrieved, and the first driving control parameter is dynamically optimized in the domain controller combined with the main laser radar and ultrasonic radar. The obtained second driving control parameter is used as the second driving instruction. The second driving instruction is transmitted to the motor controller for instruction analysis, and the second driving control parameter is obtained. The second driving control parameter is transmitted to the driving motor system to drive the motor. This solves the technical problem of insufficient intelligent driving control of the electric vehicle mobile charging device in the prior art, and improves the intelligent driving control by intelligently planning the path and dynamically optimizing the driving control parameter.

[0064] Embodiment two, based on the same inventive concept as the mobile charging device driving control method for electric vehicles in the preceding embodiment, as Figure 2 As shown in the preceding embodiment, the present application provides a mobile charging device driving control system for electric vehicles, wherein the system comprises:

[0065] The instruction receiving module 11 is configured to receive a first driving instruction through a receiving controller; the instruction analysis module 12 is configured to transmit the first driving instruction to a motor controller for instruction analysis, and obtain a first driving control parameter; the identification module 13 is configured to identify the source of the first driving instruction, and obtain a first source identification result; the optimization module 14 is configured to, when the first source identification result is from the domain controller, call a planned path of an intelligent positioning navigation system, dynamically optimize the first driving control parameter in the domain controller in combination with a main laser radar and an ultrasonic radar, and obtain a second driving control parameter as a second driving instruction; and the driving module 15 is configured to transmit the second driving instruction to the motor controller for instruction analysis, obtain a second driving control parameter, and transmit the second driving control parameter to a driving motor system to drive the motor.

[0066] Further, the driving module 15 is configured to execute the following method:

[0067] When the first source identification result is from the Bluetooth transmission module, the first driving control parameter is transmitted to the driving motor system to drive the motor.

[0068] Further, the driving module 15 is configured to execute the following method:

[0069] The domain controller, the intelligent positioning navigation system, the receiving controller, the motor controller and the driving motor system are connected to each other through a CAN bus.

[0070] Further, the optimization module 14 is configured to execute the following method:

[0071] The main laser radar and the ultrasonic radar are used to continuously scan the environment, and dynamic environment information and static environment information are obtained; a GPS loaded on the charging device is used to perform real-time positioning, and a real-time position is determined; the dynamic environment information and the static environment information, and the real-time position are combined to dynamically adjust the planned path, and a first adjusted planned path is obtained; a parameter optimizer arranged in the domain controller is used to optimize the first driving control parameter according to the first adjusted planned path, and the second driving control parameter is obtained.

[0072] Further, the optimization module 14 is configured to execute the following method:

[0073] The main laser radar emits a laser beam, scans the surrounding environment according to a preset scanning period through a rotating scanning mode, collects a periodic laser signal set sequence, traverses the periodic laser signal set sequence to generate a three-dimensional point cloud map, and obtains an environment three-dimensional point cloud map sequence; and performs dynamic iterative identification on the environment three-dimensional point cloud map sequence to determine a moving direction set and a moving speed set of a moving target set, and uses the moving direction set and the moving speed set as dynamic environment information.

[0074] Further, the optimization module 14 is configured to perform the following method:

[0075] The dynamic environment information and the static environment information are used as interference factors, a position of the real-time position in the planning path is used as an adjustment point, the path adjuster is used to adjust the planning path according to the adjustment point and the interference factors, and a first adjusted planning path is obtained.

[0076] Further, the instruction analysis module 12 is configured to perform the following method:

[0077] The historical driving log sequence is called in a preset backtracking window, a driving deviation factor-deviation direction set is obtained based on the historical driving log sequence, the driving deviation factor-deviation direction set is screened and identified to determine a target driving deviation factor-deviation direction, and the first driving control parameter is corrected based on the target driving deviation factor-deviation direction.

[0078] Further, the instruction analysis module 12 is configured to perform the following method:

[0079] The driving deviation factor-deviation direction sets are aggregated in the same category to obtain K clustering driving deviation factor-deviation direction sets, wherein K is a positive integer; the sizes of the data amounts in the K clustering driving deviation factor-deviation direction sets are counted, sets with data amounts lower than a preset data amount threshold are excluded, and M screened clustering driving deviation factor-deviation direction sets are obtained, wherein each screened clustering driving deviation factor-deviation direction set includes an identification weight, and M is a positive integer less than or equal to K; center identification is performed on the M screened clustering driving deviation factor-deviation direction sets to determine M center screened clustering driving deviation factor-deviation directions, and a target driving deviation factor-deviation direction is obtained by weighted calculation combined with the corresponding identification weights.

[0080] Further, the instruction analysis module 12 is configured to perform the following method:

[0081] The M screened clustering driving deviation factor-deviation direction sets are subjected to mean shift identification respectively to obtain the M center screened clustering driving deviation factor-deviation directions.

[0082] It should be noted that the above-mentioned embodiment sequences of the present application are merely for description only, but not for representing the advantages and disadvantages of the embodiments. And the above-mentioned embodiments of the present specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0083] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0084] The specification and drawings are merely exemplary of the present application, and any and all modifications, variations, combinations or equivalents that are within the scope of the present application should be included. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A mobile charging device drive control method for an electric vehicle, characterized by, The method is applied to a drive control system of a charging device, and the drive control system comprises a domain controller, an intelligent positioning navigation system, a receiving controller, a motor controller and a drive motor system, and the method comprises the following steps: receiving a first drive instruction through the receiving controller; transmitting the first drive instruction to the motor controller for instruction analysis to obtain first drive control parameters; performing source identification on the first drive instruction to obtain a first source identification result; when the first source identification result is from the domain controller, calling a planned path of the intelligent positioning navigation system, dynamically optimizing the first drive control parameters in the domain controller in combination with a main laser radar and an ultrasonic radar, and taking obtained second drive control parameters as second drive instructions; transmitting the second drive instructions to the motor controller for instruction analysis to obtain second drive control parameters, and transmitting the second drive control parameters to the drive motor system to drive the motor.

2. The mobile charging apparatus drive control method for an electric vehicle according to claim 1, characterized by, when the first source identification result is from a Bluetooth transmission module, transmitting the first drive control parameters to the drive motor system to drive the motor.

3. The mobile charging apparatus drive control method for an electric vehicle according to claim 1, wherein The domain controller, the intelligent positioning navigation system, the receiving controller, the motor controller and the drive motor system are connected to each other through a CAN bus.

4. The mobile charging apparatus drive control method for an electric vehicle according to claim 1, characterized by, when the first source identification result is from the domain controller, calling a planned path of the intelligent positioning navigation system, dynamically optimizing the first drive control parameters in the domain controller in combination with a main laser radar and an ultrasonic radar, and taking obtained second drive control parameters as second drive instructions, comprising: continuously scanning the environment based on the main laser radar and the ultrasonic radar to obtain dynamic environment information and static environment information; performing real-time positioning by using a GPS loaded on the charging device to determine a real-time position; dynamically adjusting the planned path in combination with the dynamic environment information and the static environment information and the real-time position to obtain a first adjusted planned path; optimizing the first drive control parameters according to the first adjusted planned path by using a parameter optimizer arranged in the domain controller to obtain the second drive control parameters.

5. The mobile charging apparatus drive control method for an electric vehicle according to claim 4, characterized by, continuously scanning the environment based on the main laser radar and the ultrasonic radar to obtain dynamic environment information and static environment information, comprising: emitting a laser beam by using the main laser radar, scanning the surrounding environment in a preset scanning period by means of rotary scanning, and collecting a periodic laser signal set sequence; generating a three-dimensional point cloud map by traversing the periodic laser signal set sequence to obtain an environment three-dimensional point cloud map sequence; dynamically and iteratively identifying the environment three-dimensional point cloud map sequence to determine a moving direction set and a moving speed set of a moving target set, and taking the moving direction set and the moving speed set as dynamic environment information.

6. The mobile charging apparatus drive control method for an electric vehicle according to claim 4, wherein taking the dynamic environment information and the static environment information as interference factors, taking a position of the real-time position in the planned path as an adjustment point, and adjusting the planned path according to the adjustment point and the interference factors by using a path adjuster to obtain a first adjusted planned path.

7. The mobile charging apparatus drive control method for an electric vehicle according to claim 1, wherein further comprising: calling a historical drive log sequence in a preset backtracking window; Obtaining a driving deviation factor-deviation direction set based on the historical driving log sequence; Screening and identifying the driving deviation factor-deviation direction set to determine a target driving deviation factor-deviation direction; Based on the target driving deviation factor-deviation direction, the first driving control parameter is corrected.

8. The mobile charging apparatus drive control method for an electric automobile according to claim 7, wherein Screening and identifying the driving deviation factor-deviation direction set to determine a target driving deviation factor-deviation direction, comprising: Performing same-class aggregation on the driving deviation factor-deviation direction set to obtain K clustering driving deviation factor-deviation direction sets, wherein K is a positive integer; Statistically determining the size of the data volume in the K clustering driving deviation factor-deviation direction sets, and excluding sets with a data volume lower than a preset data volume threshold to obtain M screening clustering driving deviation factor-deviation direction sets, wherein each screening clustering driving deviation factor-deviation direction set includes an identification weight, and M is a positive integer less than or equal to K; Iterating through the M screening clustering driving deviation factor-deviation direction sets to identify centers and determine M center screening clustering driving deviation factors-deviation directions, and performing weighted calculation based on the corresponding identification weights to obtain a target driving deviation factor-deviation direction.

9. The mobile charging apparatus drive control method for an electric automobile according to claim 8, wherein Respectively performing mean shift identification on the M screening clustering driving deviation factor-deviation direction sets to obtain the M center screening clustering driving deviation factor-deviation directions.

10. A mobile charging apparatus drive control system for an electric vehicle, characterized by, The mobile charging device driving control method for electric vehicles of any one of claims 1-9, the driving control system comprises a domain controller, an intelligent positioning and navigation system, a receiving controller, a motor controller, a driving motor system, and the system further comprises: An instruction receiving module for receiving a first driving instruction through the receiving controller; An instruction analysis module for transmitting the first driving instruction to the motor controller for instruction analysis to obtain a first driving control parameter; An identification module for source identification of the first driving instruction to obtain a first source identification result; An optimization module for, when the first source identification result is from the domain controller, calling the planned path of the intelligent positioning and navigation system, dynamically optimizing the first driving control parameter in the domain controller based on the main laser radar and ultrasonic radar, and taking the obtained second driving control parameter as a second driving instruction; A driving module for transmitting the second driving instruction to the motor controller for instruction analysis to obtain a second driving control parameter, and transmitting the second driving control parameter to the driving motor system to drive the motor.

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