A processing method for a self-generating system of an electric vehicle hub motor

Abstract

The present invention collects relevant data of the running vehicle and uploads it to the vehicle operation data analysis system for calculation and analysis, and determines whether the electric vehicle wheel hub motor self-generation system should be used according to the data results; when the running vehicle applies the electric vehicle wheel hub motor self-generation system, all the driving-related data are automatically saved to the vehicle operation data analysis system; the corresponding application type of the electric vehicle wheel hub motor self-generation system is obtained according to the data analysis results; deep learning is performed on the existing data in the personal vehicle database, and a good training model has been formed, which will first be used to predict the user's vehicle slow-stop habits; the electric vehicle wheel hub motor self-generation technology utilizes the inertial kinetic energy of the running vehicle to recover and convert it into electrical energy, which not only has a high degree of intelligent energy recovery for the electric vehicle, but can also be combined with the user's habits for the purpose of slow-stopping, and also reduces pollution to the atmospheric environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of self-generation of electric vehicle wheel hub motors, and in particular, relates to a processing method for a self-generation system of an electric vehicle wheel hub motor. Background Art

[0002] Electric vehicles are a common means of transportation in our daily travel, and they play a very important role in improving people's traffic efficiency and facilitating their lives. However, when electric vehicles are driving, too much unreasonable energy loss causes the battery power to decrease rapidly, which will seriously reduce people's travel range and increase travel costs.

[0003] Therefore, energy recovery for electric vehicles is a great demand for people's travel. For electric vehicles in motion, it is necessary to combine relevant data calculations and judgments, and then use the electric vehicle hub motor self-generation technology to perform reasonable energy recovery; however, for existing electric vehicles, there is no recovery of unreasonable energy loss during driving, and the recovery operation is not intelligent; on the one hand, the existing electric vehicles have a low degree of intelligence in energy recovery for electric vehicles and cannot be combined with user habits for the purpose of slowing down and stopping, which not only causes a lot of energy loss during driving, but also greatly reduces the expected driving distance; on the other hand, when the electric vehicle is driving, the energy used cannot be recovered in a timely and reasonable manner, which increases the pollution to the atmospheric environment. Summary of the invention

[0004] The present invention is based on the above technical problems and proposes a method for processing the self-generation system of the electric vehicle hub motor for the use of the electric vehicle hub motor for self-generation. Not only is the energy recovery of the electric vehicle highly intelligent and can be slowed down according to user habits, but it also reduces pollution to the atmospheric environment.

[0005] The present invention is achieved in that:

[0006] A method for processing a self-generating system of an electric vehicle hub motor, the method using a vehicle operation data collector, a vehicle operation data analysis system, an electric energy storage device and a personal vehicle database; the method comprises the following steps:

[0007] Step 1: When the driver completely releases the accelerator while driving the vehicle, the vehicle operation data collector collects the relevant data of the driving vehicle and automatically uploads it to the vehicle operation data analysis system;

[0008] Step 2: Calculate and analyze the relevant data of the running vehicle based on the vehicle operation data analysis system, and determine whether the electric vehicle hub motor self-generation system can be used based on the data results; the operation method of applying this system is as follows:

[0009] Step 2.1: The relevant data of the running vehicle is automatically uploaded to the vehicle operation data analysis system;

[0010] Step 2.2: The vehicle operation data analysis system calculates, processes and analyzes the relevant data of the running vehicle;

[0011] Step 2.3: Determine whether the electric vehicle hub motor self-generation system can be used based on the calculation results;

[0012] Step 3: When the electric vehicle wheel hub motor self-generation system is applied to the driving vehicle, all driving-related data are automatically saved to the vehicle operation data analysis system; the application type of the electric vehicle wheel hub motor self-generation system is identified and judged. If it is a vehicle overspeeding type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 4 is executed; if it is a vehicle normal speed type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 5 is executed; if it is a vehicle critical floating speed type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 6 is executed;

[0013] Step 4: The moving vehicle increases the wheel hub driving resistance of the electric vehicle through the self-generating system of the electric vehicle wheel hub motor to reduce the speed until the driving speed collected by the data collector matches the vehicle operation data analysis system and meets the range value of the vehicle's normal speed type, and then executes step 5;

[0014] Step 5: The moving vehicle recovers the inertial kinetic energy according to the normal speed type of the vehicle. In this process, no other energy is involved, and only the vehicle slowly stops. The self-generating system of the automobile hub motor converts the inertial kinetic energy of the moving vehicle into electrical energy; when the moving vehicle slows down to a certain range, the person steps on the brake until the vehicle stops completely, and the self-generating system of the electric vehicle hub motor automatically shuts down;

[0015] Step 6: The speed value of the moving vehicle meets the requirement of starting the electric vehicle wheel hub motor self-generation system, but the vehicle speed value does not meet the requirement of starting this system in a short time; the electric vehicle wheel hub motor self-generation system will recover the vehicle inertial kinetic energy based on the vehicle speed collected by the vehicle operation data collector. If the vehicle speed still meets the requirement of applying this electric vehicle wheel hub motor self-generation system within the specified time range, the moving vehicle will recover the vehicle inertial kinetic energy. In this process, the vehicle inertial kinetic energy is converted into electrical energy by the vehicle inertial kinetic energy during the slow stopping of the vehicle without relying on the intervention of other energies. When the moving vehicle slows down to a certain range, the person steps on the brake until the vehicle stops completely, and the electric vehicle wheel hub motor self-generation system automatically shuts down.

[0016] Step 7: The vehicle operation data collector collects the relevant data of the running vehicle and the user's driving and stopping habits to form a personal vehicle database, which will be saved in the vehicle operation data analysis system. The personal vehicle database will be deeply learned, and the subsequent relevant data of vehicle operation will be retained and updated in real time. The training model that has been formed for the personal vehicle database will first be used to predict the user's driving and stopping habits.

[0017] The specific operation method of the vehicle operation data analysis system for calculation and analysis in step 2.2 includes:

[0018] Assume that the vehicle speed collected by the vehicle operation data collector is The vehicle operation data analysis system sets the minimum vehicle speed of the vehicle application system to V0, and the vehicle deceleration acceleration to a;

[0019] According to the algorithm formula:

[0020]

[0021] t0 represents the time from releasing the accelerator to stopping the application system of the moving vehicle;

[0022] The specific operation method for determining the calculation result of the vehicle operation data analysis system in step 2.3 includes:

[0023] The relevant data of the driving vehicle is calculated. According to the results, the following conditions must be met at the same time before the electric vehicle hub motor self-generation system can be applied:

[0024]

[0025] If the vehicle speed value does not meet the requirements for starting the system in a short period of time, it means that the driving vehicle speed value does not meet the requirements for the vehicle application system minimum vehicle speed stabilization time range of 1s; if the vehicle speed still meets the requirements for the application of the electric vehicle hub motor self-generation system within the specified time range, the driving vehicle will recover the vehicle inertia kinetic energy; it means that the driving vehicle speed value meets the requirements for the vehicle application system minimum vehicle speed stabilization time range of 3s;

[0026] The moving vehicle meets the requirements of the electric vehicle wheel hub motor self-generation system. The moving vehicle recovers the vehicle's inertial kinetic energy. In this process, it does not rely on the intervention of other energy sources. It only relies on the vehicle's slow stop. The vehicle's wheel hub motor self-generation system converts the moving vehicle's inertial kinetic energy into electrical energy. The vehicle operation data collector collects the potential difference of the electric field in the wheel hub motor as follows: W is the work done by the electric field force when the charged particles move in the electric field, and Q is the charge carried by the conductor in the wheel hub motor;

[0027] Assume that the vehicle speed collected by the vehicle operation data collector is The work done by the moving vehicle is W0; the weight of the vehicle is The vehicle operation data analysis cloud platform sets the vehicle speed when the vehicle is completely stopped as V0;

[0028] According to the algorithm formula:

[0029]

[0030] W0 represents the kinetic energy of the moving vehicle from the time the accelerator is released to the time the vehicle stops completely;

[0031] According to the algorithm formula:

[0032]

[0033] W is the work done by the electric field force when the charged particle moves in the electric field, Q is the charge carried by the conductor in the wheel hub motor, and the potential difference of the electric field in the wheel hub motor is U;

[0034] According to the algorithm formula:

[0035]

[0036] Q is the amount of electricity carried by the conductor in the wheel hub motor, that is, the amount of electricity generated when the electric vehicle's wheel hub motor self-generation system is used to speed up the vehicle from the time the accelerator is released to the time the vehicle comes to a complete stop, and the electricity is stored in the energy storage device; when the vehicle screen prompts that the on-board battery is low, the energy storage device is automatically started as a backup battery, and personnel can use the electricity in the energy storage device to power the vehicle.

[0037] In step 1, the vehicle operation data collector collects the relevant data of the running vehicle. The specific operation method includes:

[0038] The vehicle operation data collector collects the relevant data of the moving vehicle: vehicle speed, vehicle weight and vehicle operation road conditions; the relevant data collection values ​​of the moving vehicle are marked as Where β = 0, 1, 2, ..., n.

[0039] Vehicle operation data analysis system: data calculation and processing module, data result analysis module, personal vehicle database and deep learning module structure; the vehicle operation data analysis system structure is divided into marking values ​​μ i , where i = 0, 1, 2, ..., n.

[0040] In step 7, the vehicle operation data analysis system forms a personal vehicle database for deep learning. The specific operation method includes:

[0041] After the electric vehicle hub motor self-generation system has been applied to the driving vehicle, the vehicle data results generated are transmitted to the personal vehicle database and saved to the vehicle operation data analysis system. The personal vehicle database is deeply learned. The trained personal vehicle database will predict the user's driving habits. The relevant data of subsequent vehicle operation will be retained, updated and retrained in real time. The personal vehicle database has formed a good training model and will first use this model to predict the user's driving habit of slowing down and stopping.

[0042] The specific operation process of deep learning is to divide the existing data in the personal vehicle database into 60% training set data and 40% test set data; first use the 60% training set data to train the neural network to form a training model;

[0043] 40% of the test set data is used to test the trained model to prevent the model from overfitting on the training set, that is, to prevent the model from learning too many features specific to the training set.

[0044] The model modified by the test set data will be used as a prediction model for the user's driving habits. When the user releases the accelerator, the database will be called first when the personal vehicle database has been formed. The relevant data of the driving vehicle will be used to predict the user's slow-stop habits through the trained model, so as to achieve the user's intelligent slow-stop effect.

[0045] When the user's vehicle stops completely without any intervention, the relevant data of the user's driving will be saved and updated as new data in the personal vehicle database. This database will also be trained and tested again to form a new model to predict the user's slow-stop habits next time;

[0046] The personal vehicle database has formed a good training model, which will first be used to predict the user's vehicle slow-down and stop habits.

[0047] Based on any of the above aspects, the beneficial effects of the present invention are:

[0048] 1. The present invention collects relevant data of the running vehicle and uploads it to the vehicle operation data analysis system for calculation and analysis, and determines whether the electric vehicle hub motor self-generation system should be used according to the data results; when the running vehicle uses the electric vehicle hub motor self-generation system, all the driving-related data are automatically saved to the vehicle operation data analysis system; the corresponding application type of the electric vehicle hub motor self-generation system is obtained according to the data analysis results; the inertial kinetic energy of the running vehicle is recovered and converted into electrical energy through the electric vehicle hub motor self-generation technology; not only the energy is recovered during driving itself, but also the expected driving distance is greatly increased; on the other hand, the electric vehicle also reduces the pollution to the atmospheric environment when driving.

[0049] 2. After the electric vehicle hub motor self-generating system has been applied to the running vehicle, the vehicle data results generated by the present invention are transmitted to the personal vehicle database and saved in the vehicle operation data analysis system, and the personal vehicle database is deeply learned. The trained personal vehicle database will predict the user's driving habits, and the subsequent vehicle operation related data and other data will be retained, updated and retrained in real time for the personal vehicle database; the personal vehicle database has formed a good training model and will first use this model to predict the user's driving habit of the vehicle; not only is the electric vehicle energy recovery highly intelligent, but it can also be combined with the user's habits for the purpose of slowing down. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0051] Figure 1 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0052] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technical personnel in the relevant technical field may make various modifications or supplements to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0053] Combination Figure 1 , a method for processing a self-generating system of an electric vehicle hub motor, the method using a vehicle operation data collector, a vehicle operation data analysis system, an electric energy storage device and a personal vehicle database; the method comprises the following steps:

[0054] Step 1: When the driver completely releases the accelerator during driving, the vehicle operation data collector collects relevant data of the vehicle and automatically uploads it to the vehicle operation data analysis system;

[0055] In a specific embodiment of the present invention, the specific operation method of the vehicle operation data collector in step 1 to collect relevant data of the traveling vehicle includes:

[0056] The vehicle operation data collector collects the relevant data of the moving vehicle: vehicle speed, vehicle weight and vehicle operation road conditions; the relevant data collection values ​​of the moving vehicle are marked as

[0057] Where β = 0, 1, 2, ..., n.

[0058] Step 2: Calculate and analyze the relevant data of the running vehicle according to the vehicle operation data analysis system, and judge whether the electric vehicle hub motor self-generation system can be used according to the data results; the operation method of applying this system is as follows:

[0059] Step 2.1: The relevant data of the running vehicle is automatically uploaded to the vehicle operation data analysis system;

[0060] In a specific embodiment of the present invention, the vehicle operation data analysis system in step 2.1 includes:

[0061] Vehicle operation data analysis system: data calculation and processing module, data result analysis module, personal vehicle database structure; the vehicle operation data analysis system structure is divided into mark values ​​μ i , where i = 0, 1, 2, ..., n.

[0062] Step 2.2: The vehicle operation data analysis system calculates, processes and analyzes the relevant data of the running vehicle;

[0063] In a specific embodiment of the present invention, the specific operation method of the vehicle operation data analysis system for calculation and analysis in step 2.2 includes:

[0064] Assume that the vehicle speed collected by the vehicle operation data collector is The vehicle operation data analysis system sets the minimum vehicle speed of the vehicle application system to V0, and the vehicle deceleration acceleration to a;

[0065] According to the algorithm formula:

[0066]

[0067] t0 represents the time from releasing the accelerator to stopping the application system of the moving vehicle.

[0068] Step 2.3: Determine whether the electric vehicle hub motor self-generation system can be used based on the calculation results;

[0069] In a specific embodiment of the present invention, the specific operation method for determining the calculation result of the vehicle operation data analysis system in step 2.3 includes:

[0070] The relevant data of the driving vehicle is calculated. According to the results, the following conditions must be met at the same time before the electric vehicle hub motor self-generation system can be applied:

[0071]

[0072] Step 3: When the electric vehicle wheel hub motor self-generation system is applied to the driving vehicle, all driving-related data are automatically saved to the vehicle operation data analysis system; the application type of the electric vehicle wheel hub motor self-generation system is identified and judged. If it is a vehicle overspeeding type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 4 is executed; if it is a vehicle normal speed type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 5 is executed; if it is a vehicle critical floating speed type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 6 is executed;

[0073] Step 4: The moving vehicle increases the wheel hub driving resistance of the electric vehicle through the self-generating system of the electric vehicle wheel hub motor to reduce the speed until the driving speed collected by the data collector matches the vehicle operation data analysis system and meets the range value of the vehicle's normal speed type, and then executes step 5;

[0074] Step 5: The moving vehicle recovers the inertial kinetic energy according to the normal speed type of the vehicle. In this process, no other energy is involved, and only the vehicle slowly stops. The self-generating system of the automobile hub motor converts the inertial kinetic energy of the moving vehicle into electrical energy; when the moving vehicle slows down to a certain range, the person steps on the brake until the vehicle stops completely, and the self-generating system of the electric vehicle hub motor automatically shuts down;

[0075] Step 6: The speed value of the moving vehicle meets the requirement of starting the electric vehicle wheel hub motor self-generation system, but the vehicle speed value does not meet the requirement of starting this system in a short time; the electric vehicle wheel hub motor self-generation system will recover the vehicle inertial kinetic energy based on the vehicle speed collected by the vehicle operation data collector. If the vehicle speed still meets the requirement of applying this electric vehicle wheel hub motor self-generation system within the specified time range, the moving vehicle will recover the vehicle inertial kinetic energy. In this process, the vehicle inertial kinetic energy is converted into electrical energy by the vehicle inertial kinetic energy during the slow stopping of the vehicle without relying on the intervention of other energies. When the moving vehicle slows down to a certain range, the person steps on the brake until the vehicle stops completely, and the electric vehicle wheel hub motor self-generation system automatically shuts down.

[0076] In a specific embodiment of the present invention, the specific operation method of the vehicle using the electric vehicle hub motor self-generation system to generate self-generation in steps 4, 5 and 6 includes:

[0077] If the vehicle speed value does not meet the requirements for starting the system in a short period of time, it means that the driving vehicle speed value does not meet the requirements for the vehicle application system minimum vehicle speed stabilization time range of 1s; if the vehicle speed still meets the requirements for the application of the electric vehicle hub motor self-generation system within the specified time range, the driving vehicle will recover the vehicle inertia kinetic energy; it means that the driving vehicle speed value meets the requirements for the vehicle application system minimum vehicle speed stabilization time range of 3s;

[0078] The moving vehicle meets the requirements of the electric vehicle wheel hub motor self-generation system, and the moving vehicle recovers the vehicle's inertial kinetic energy. In this process, it does not rely on the intervention of other energy sources, but only relies on the vehicle's slow stopping process. The vehicle's wheel hub motor self-generation system converts the moving vehicle's inertial kinetic energy into electrical energy; the vehicle operation data collector collects the potential difference of the electric field in the wheel hub motor as P4, W is the work done by the electric field force when the charged particles move in the electric field, and Q is the amount of electricity carried by the conductor in the wheel hub motor;

[0079] According to the law of conservation of energy, the energy of an object will not increase or decrease out of thin air. If kinetic energy decreases, it will be converted into other forms of energy. To be converted into electrical energy, kinetic energy must decrease.

[0080] ΔE k =W=UQ, U refers to the potential difference;

[0081] Electric work calculation formula: W = UQ

[0082] Electric energy is also a kind of energy, and the implementer of this energy is the electric charge. The amount of electric charge is the implementer of this energy in a general time from point A to point B. The work done by each charge from point A to point B is the electric work. The multiplication of the two is the electric work of AB, which is the consumed electric energy.

[0083] Assume that the vehicle speed collected by the vehicle operation data collector is The work done by the moving vehicle is W0; the weight of the vehicle is The vehicle operation data analysis cloud platform sets the vehicle speed when the vehicle is completely stopped as V0;

[0084] According to the algorithm formula:

[0085]

[0086] W0 represents the kinetic energy of the moving vehicle from the time the accelerator is released to the time the vehicle stops completely;

[0087] According to the algorithm formula:

[0088]

[0089] W is the work done by the electric field force when the charged particle moves in the electric field, Q is the charge carried by the conductor in the wheel hub motor, and the potential difference of the electric field in the wheel hub motor is U;

[0090] According to the algorithm formula:

[0091]

[0092] Q is the amount of electricity carried by the conductor in the wheel hub motor, that is, the amount of electricity generated when the electric vehicle's wheel hub motor self-generation system is used to speed up the vehicle from the time the accelerator is released to the time the vehicle comes to a complete stop, and the electricity is stored in the energy storage device; when the vehicle screen prompts that the on-board battery is low, the energy storage device is automatically started as a backup battery, and personnel can use the electricity in the energy storage device to power the vehicle.

[0093] Step 7: The vehicle operation data collector collects the relevant data of the running vehicle and the user's driving and stopping habits to form a personal vehicle database, which is saved in the vehicle operation data analysis system and updated in real time;

[0094] In a specific embodiment of the present invention, the specific operation method of the vehicle operation data analysis system in step 7 forming a personal vehicle database for deep learning includes:

[0095] After the electric vehicle hub motor self-generation system has been applied to the driving vehicle, the vehicle data results generated are transmitted to the personal vehicle database and saved to the vehicle operation data analysis system. The personal vehicle database is deeply learned. The trained personal vehicle database will predict the user's driving habits. The relevant data of subsequent vehicle operation will be retained, updated and retrained in real time. The personal vehicle database has formed a good training model, which will first use this model to predict the user's driving habit of slowing down.

[0096] The specific operation process of deep learning is to divide the existing data in the personal vehicle database into 60% training set data and 40% test set data; first use the 60% training set data to train the neural network to form a training model;

[0097] 40% of the test set data is used to test the trained model to prevent the model from overfitting on the training set, that is, to prevent the model from learning too many features specific to the training set.

[0098] The model modified by the test set data will be used as a prediction model for the user's driving habits; when the user releases the accelerator, this database will be called first when the personal vehicle database has been formed, and the relevant data of the driving vehicle will be used through the trained

[0099] The trained model can predict the user's slow-down habits, achieving the user's intelligent slow-down effect;

[0100] When the user's vehicle stops completely without any intervention, the relevant data of the user's driving will be saved and updated as new data in the personal vehicle database. This database will also be trained and tested again to form a new model to predict the user's slow-stop habits next time;

[0101] The personal vehicle database has formed a good training model, which will first be used to predict the user's vehicle slow-down and stop habits.

[0102] The present invention collects relevant data of the running vehicle and uploads it to the vehicle operation data analysis system for calculation and analysis, and determines whether the electric vehicle wheel hub motor self-generation system should be used according to the data results; when the running vehicle applies the electric vehicle wheel hub motor self-generation system, all the driving-related data are automatically saved to the vehicle operation data analysis system; the corresponding application type of the electric vehicle wheel hub motor self-generation system is obtained according to the data analysis results; deep learning is performed on the existing data in the personal vehicle database, and a good training model has been formed, which will first be used to predict the user's vehicle slow-stop habits; the electric vehicle wheel hub motor self-generation technology utilizes the inertial kinetic energy of the running vehicle to recover and convert it into electrical energy, which not only has a high degree of intelligent energy recovery for the electric vehicle, but can also be combined with the user's habits for the purpose of slow-stopping, and also reduces pollution to the atmospheric environment.

[0103] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for processing a self-generating system of an electric vehicle hub motor, the method using a vehicle operation data collector, a vehicle operation data analysis system, an electric energy storage device and a personal vehicle database; characterized in that: The method comprises the following steps: Step 1: When the driver completely releases the accelerator while driving the vehicle, the vehicle operation data collector collects the relevant data of the driving vehicle and automatically uploads it to the vehicle operation data analysis system; Step 2: Calculate and analyze the relevant data of the running vehicle based on the vehicle operation data analysis system, and determine whether the electric vehicle hub motor self-generation system can be used based on the data results; the operation method of applying this system is as follows: Step 2.1: The relevant data of the running vehicle is automatically uploaded to the vehicle operation data analysis system; Step 2.2: The vehicle operation data analysis system calculates, processes and analyzes the relevant data of the running vehicle; Step 2.3: Determine whether the electric vehicle hub motor self-generation system can be used based on the calculation results; Step 3: When the electric vehicle wheel hub motor self-generation system is applied to the driving vehicle, all driving-related data are automatically saved to the vehicle operation data analysis system; the application type of the electric vehicle wheel hub motor self-generation system is identified and judged. If it is a vehicle overspeeding type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 4 is executed; if it is a vehicle normal speed type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 5 is executed; if it is a vehicle critical floating speed type, the corresponding processing method is extracted from the vehicle operation data analysis system, and then adjusted according to the processing method, and step 6 is executed; Step 4: The moving vehicle increases the wheel hub driving resistance of the electric vehicle through the self-generating system of the electric vehicle wheel hub motor to reduce the speed until the driving speed collected by the data collector matches the vehicle operation data analysis system and meets the range value of the vehicle's normal speed type, and then executes step 5; Step 5: The moving vehicle recovers the inertial kinetic energy according to the normal speed type of the vehicle. In this process, no other energy is involved, and only the vehicle slowly stops. The self-generating system of the automobile hub motor converts the inertial kinetic energy of the moving vehicle into electrical energy; when the moving vehicle slows down to a certain range, the person steps on the brake until the vehicle stops completely, and the self-generating system of the electric vehicle hub motor automatically shuts down; Step 6: The speed value of the moving vehicle meets the requirement of starting the electric vehicle wheel hub motor self-generation system, but the vehicle speed value does not meet the requirement of starting this system in a short time; the electric vehicle wheel hub motor self-generation system will recover the vehicle inertial kinetic energy based on the vehicle speed collected by the vehicle operation data collector. If the vehicle speed still meets the requirement of applying this electric vehicle wheel hub motor self-generation system within the specified time range, the moving vehicle will recover the vehicle inertial kinetic energy. In this process, the vehicle inertial kinetic energy is converted into electrical energy by the vehicle inertial kinetic energy during the slow stopping of the vehicle without relying on the intervention of other energies. When the moving vehicle slows down to a certain range, the person steps on the brake until the vehicle stops completely, and the electric vehicle wheel hub motor self-generation system automatically shuts down. Step 7: The vehicle operation data collector collects the relevant data of the running vehicle and the user's driving and stopping habits to form a personal vehicle database, which will be saved in the vehicle operation data analysis system. The personal vehicle database will be deeply learned, and the subsequent relevant data of vehicle operation will be retained and updated in real time. The training model that has been formed for the personal vehicle database will first be used to predict the user's driving and stopping habits. The specific operation method of the vehicle operation data analysis system for calculation and analysis in step 2.2 includes: Assume that the vehicle speed collected by the vehicle operation data collector is The vehicle operation data analysis system sets the minimum vehicle speed of the vehicle application system to V0, and the vehicle deceleration acceleration to a; According to the algorithm formula: t0 represents the time from releasing the accelerator to stopping the application system of the moving vehicle; The specific operation method for determining the calculation result of the vehicle operation data analysis system in step 2.3 includes: The relevant data of the driving vehicle is calculated. According to the results, the following conditions must be met at the same time before the electric vehicle hub motor self-generation system can be applied: If the vehicle speed value does not meet the requirements for starting the system in a short period of time, it means that the driving vehicle speed value does not meet the requirements for the vehicle application system minimum vehicle speed stabilization time range of 1s; if the vehicle speed still meets the requirements for the application of the electric vehicle hub motor self-generation system within the specified time range, the driving vehicle will recover the vehicle inertia kinetic energy; it means that the driving vehicle speed value meets the requirements for the vehicle application system minimum vehicle speed stabilization time range of 3s; The moving vehicle meets the requirements of the electric vehicle wheel hub motor self-generation system. The moving vehicle recovers the vehicle's inertial kinetic energy. In this process, it does not rely on the intervention of other energy sources. It only relies on the vehicle's slow stop. The vehicle's wheel hub motor self-generation system converts the moving vehicle's inertial kinetic energy into electrical energy. The vehicle operation data collector collects the potential difference of the electric field in the wheel hub motor as follows: W is the work done by the electric field force when the charged particles move in the electric field, and Q is the charge carried by the conductor in the wheel hub motor; Assume that the vehicle speed collected by the vehicle operation data collector is The work done by the moving vehicle is W0; the weight of the vehicle is The vehicle operation data analysis cloud platform sets the vehicle speed when the vehicle is completely stopped as V0; According to the algorithm formula: W0 represents the kinetic energy of the moving vehicle from the time the accelerator is released to the time the vehicle stops completely; According to the algorithm formula: W is the work done by the electric field force when the charged particle moves in the electric field, Q is the charge carried by the conductor in the wheel hub motor, and the potential difference of the electric field in the wheel hub motor is U; According to the algorithm formula: Q is the amount of electricity carried by the conductor in the wheel hub motor, that is, the amount of electricity generated when the electric vehicle's wheel hub motor self-generation system is used to speed up the vehicle from the time the accelerator is released to the time the vehicle comes to a complete stop, and the electricity is stored in the energy storage device; when the vehicle screen prompts that the on-board battery is low, the energy storage device is automatically started as a backup battery, and personnel can use the electricity in the energy storage device to power the vehicle.

2. The method for processing the self-generating system of the electric vehicle hub motor according to claim 1, characterized in that: The specific operation method of the vehicle operation data collector in step 1 to collect relevant data of the running vehicle includes: The vehicle operation data collector collects the relevant data of the moving vehicle: vehicle speed, vehicle weight and vehicle operation road conditions; the relevant data collection values ​​of the moving vehicle are marked as Where β = 0, 1, 2, ..., n.

3. A method for processing a self-generating system of an electric vehicle hub motor according to claim 1, Features: The vehicle operation data analysis system in step 2.1 includes: Vehicle operation data analysis system: data calculation and processing module, data result analysis module, personal vehicle database and deep learning module structure; the vehicle operation data analysis system structure is divided into marking values ​​μ i , where i = 0, 1, 2, ..., n.

4. The method for processing the self-generating system of the electric vehicle hub motor according to claim 1, characterized in that: The specific operation method of the vehicle operation data analysis system in step 7 forming a personal vehicle database for deep learning includes: After the electric vehicle hub motor self-generation system has been applied to the driving vehicle, the generated vehicle data results are transmitted to the personal vehicle database and saved to the vehicle operation data analysis system. The personal vehicle database is deeply learned. The trained personal vehicle database will predict the user's driving habits. The relevant data of subsequent vehicle operation will be retained, updated and retrained in real time. The personal vehicle database has formed a good training model and will first use this model to predict the user's driving habit of the vehicle.

5. A vehicle-mounted system, characterized in that: According to the multi-structure processing in the vehicle operation data analysis system, including: a data calculation processing module, a data result analysis module, and a personal vehicle database structure; the running vehicles that upload relevant data are divided according to the running speed, and the vehicle inertial kinetic energy is accurately recovered and converted into electrical energy; the self-generation of the electric vehicle hub motor is calculated and analyzed by the on-board system to execute the method described in any one of claims 1 to 4 above.

6. A vehicle-mounted system, characterized in that: The method described in any one of claims 1 to 4 is implemented by relying on the vehicle system calculation and analysis service program to realize the self-generation of electric vehicle hub motor.

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