Balance car capable of automatically adjusting running posture

Abstract

The invention relates to a balance car capable of automatically adjusting a driving posture, and belongs to the technical field of balance car posture control. Comprising a state detection unit, an attitude control unit and a motor driving unit which are in communication connection in sequence; the attitude control unit performs real-time road surface recognition and vehicle body attitude calculation according to the received data, and outputs a driving attitude control signal according to a driving attitude control strategy; the state detection unit is used for collecting wheel rotation state data and vehicle body running posture data; and the motor driving unit drives a motor according to the driving posture control signal, so that the balance car keeps a balance posture and continues to run. According to the method, the running speed limit value is obtained according to the speed-torque fitting curve through the conditions of the running scene, the posture adjustment control signal is obtained by combining vehicle body posture calculation, the control force of balance vehicle posture adjustment is guaranteed, and the safety of balance vehicle posture adjustment is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of posture control of a balancing vehicle, and in particular relates to a balancing vehicle with automatic adjustment of driving posture. Background Art

[0002] The self-balancing scooter is a kind of intelligent transportation tool widely used in recent years, which realizes stable driving through self-balancing technology. It uses gyroscope and acceleration sensor to sense the posture change of the vehicle body, and adjusts the speed and direction of the wheel in real time through the motor drive system to maintain the balance of the vehicle.

[0003] However, in actual use, the following problems still exist: the self-balancing scooter has poor adaptability to complex road surfaces, such as bumpy roads, gravel roads, etc., and the body's posture adjustment ability is limited, which can easily lead to imbalance; the self-balancing scooter system is mostly designed based on a preset standard user model, and has poor adaptability to differences in user weight, resulting in unstable situations for different users during use; in the process of posture adjustment, the control force of the self-balancing scooter during operation is not considered. When dealing with complex roads, the motor needs to provide greater torque to keep the body balanced. When the self-balancing scooter is underpowered, it is difficult to maintain balance.

[0004] Therefore, it is urgent to propose a balancing vehicle with automatic adjustment of driving posture, so as to improve the effectiveness of posture control by detecting the conditions of application scenarios. Summary of the invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a self-balancing vehicle with automatic driving posture adjustment. The operating speed limit is obtained according to the speed-torque fitting curve based on the conditions of the operating scene, and the posture adjustment control signal is obtained in combination with the vehicle body posture calculation, thereby ensuring the control force of the self-balancing vehicle posture adjustment and improving the safety of the self-balancing vehicle posture adjustment.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present disclosure provides a self-balancing vehicle with automatic driving posture adjustment, comprising a state detection unit, a posture control unit and a motor drive unit, wherein the state detection unit, the posture control unit and the motor drive unit are sequentially connected in communication; The posture control unit performs real-time road surface recognition and vehicle posture calculation based on the received data, and outputs a driving posture control signal according to the driving posture control strategy; Wherein, the driving posture control strategy includes: Identify the real-time road surface and detect the peak power of the balancing vehicle battery; The maximum operating speed of the balancing vehicle is obtained through the speed-torque fitting curve according to the road surface recognition results and the battery peak power; According to the calculation results of the vehicle posture, the PID algorithm is used to output the control signal of the anti-overturning torque to maintain the balanced posture of the balancing vehicle; Monitor the rotation status of the balance vehicle tires. When a single tire is in a locked state, perform a deceleration turn. When both tires are in a locked state at the same time, calculate and output the control signal of the anti-overturning torque according to the vehicle body posture.

[0007] As a preferred technical solution of the present invention, the real-time road surface recognition comprises the following steps: S11, data preprocessing: performing denoising on the wheel rotation state data and smoothing the signal using a smoothing algorithm; S12, road surface feature extraction: feature extraction is completed by performing time domain analysis and frequency domain analysis on the data, and a real-time road surface feature set is established based on the selected road surface related features; S13, road surface classification: perform similarity matching between the collected real-time road surface feature set and the test road surface feature set to complete rapid road surface identification, wherein the test road surface feature set is obtained by experimentally testing the time domain and frequency domain response differences of the balance vehicle on the selected road surface.

[0008] As a preferred technical solution of the present invention, the acquisition of the test road surface feature set includes the following steps: S131. Select the required road surface: select the test road surface according to the actual application scenario of the self-balancing vehicle; the test road surface includes sand and gravel, tile, cement and asphalt road surface; S132, collecting road surface data: collecting response data of the test road surface through a three-axis acceleration sensor installed inside the tire; S133, data feature extraction: after preprocessing the collected data of each test road surface, characteristic values ​​are obtained through time domain analysis and frequency domain analysis, and features are selected according to the correlation with road surface identification to establish feature sets of different road surfaces; The selected road surface time domain features include mean, standard deviation, maximum, minimum, zero-crossing rate and signal energy, and the frequency domain features include main frequency, frequency band energy and frequency center.

[0009] As a preferred technical solution of the present invention, the vehicle posture calculation comprises the following steps: S21, inclination calculation: calculate the change of inclination through the output of the gyroscope, and obtain the current inclination through integration; calculate the current inclination using the output of the accelerometer; S22, complementary filtering: setting the trust weights of the gyroscope and the accelerometer, and performing weighted averaging on the measurement results of the two sensors according to the weights to form a fused inclination angle; The fusion formula is: ; In the formula, is the inclination angle after fusion, is the inclination angle obtained by the gyroscope, is the inclination angle obtained by the accelerometer, a is the trust weight of the gyroscope measurement, is the angular velocity measured by the gyroscope.

[0010] As a preferred technical solution of the present invention, the establishment of the speed-torque fitting curve includes the following steps: S31. Simulate the use process of users with different quality levels and test the maximum anti-rollover moment of each selected required road surface, including road driving and wheel locking test states; S32, selecting a quality grade and a test road surface, obtaining and recording the maximum anti-overturning moment in the test state at different speeds; S33. After multiple tests, the speed-torque fitting curves of users of different quality levels under each road condition are established.

[0011] As a preferred technical solution of the present invention, the detection of the peak power of the battery of the balancing vehicle includes the following steps: S41, setting constraint conditions: selecting the state of charge as a constraint condition affecting the safe operation of the battery, and setting the state of charge to be no less than 5%; S42, SOC estimation based on circuit model: Selecting a first-order RC model for SOC estimation, including the following steps: Preliminary parameter identification: Use the extended Kalman filter algorithm to identify battery parameters online; Preliminary SOC estimation: The EKF algorithm is used to make a preliminary estimate of the SOC. The EKF calculates the current SOC value based on the input and output data of the battery and the first-order RC model. Introducing multiple innovation theory: Introducing multiple innovation theory to expand the unscented Kalman filter algorithm; MIUKF algorithm execution: define the innovation matrix and gain matrix, and use the extended unscented Kalman filter algorithm to update the SOC; Obtain the final result: Through multiple iterations of the MIUKF algorithm, the final SOC estimation value is formed; S43, SOP estimation based on SOC constraints: Calculate n The change of SOC after a discharge cycle ensures that SOC does not fall below the set minimum limit SOC min , according to the change of SOC, calculate the maximum allowable discharge peak current.

[0012] As a preferred technical solution of the present invention, the maximum discharge peak current Imax , the calculation formula is: ; In the formula, C max is the maximum capacity of the battery, SOC k Indicates at time k The state of charge, SOC min Indicates the battery's lowest state of charge. n is the number of discharge cycles, T s is the length of the sampling period.

[0013] As a preferred technical solution of the present invention, the state detection unit includes an accelerometer installed on the inner wall of the tire and a posture sensor installed on the vehicle body, which is used to collect wheel rotation state data and vehicle body running posture data, and send the collected data to the posture control unit.

[0014] As a preferred technical solution of the present invention, the wheel rotation state data is collected by an accelerometer on the inner wall of the tire, and the vehicle body running posture data is collected by a posture sensor in the vehicle body; The attitude sensor includes a gyroscope and an acceleration sensor, which are used to measure acceleration and angular velocity simultaneously.

[0015] As a preferred technical solution of the present invention, the motor drive unit drives the motor according to the driving posture control signal, so that the balancing vehicle maintains a balanced posture and continues to run; The motor drive unit realizes the direction and speed control of the motor through the motor drive circuit, wherein the motor drive circuit adopts a current drive chip to control the direction and speed of the motor.

[0016] The beneficial effects of the present invention are: The present invention realizes the adjustment of the posture of the balance vehicle through two parts of detection, including driving road surface recognition and vehicle body posture detection. According to the vehicle body posture calculation result, the PID algorithm is used to output the control signal of the anti-overturning torque to maintain the balanced posture of the balance vehicle body. At the same time, the driving speed is limited according to the speed-torque fitting curve according to the conditions of the operating scene to ensure that the control force of the balance vehicle posture adjustment is sufficient, thereby improving the operating safety of the balance vehicle and ensuring that the balance vehicle can sufficiently adjust the overturning state in the emergency situation of tire locking.

[0017] In the process of road surface recognition, the present invention extracts features based on data collected by an acceleration sensor installed inside a tire, and quickly classifies the real-time road surface in combination with a test road surface feature set, thereby providing a basis for speed limit control, and at the same time improving the recognition response speed through similarity classification; in the calculation of the vehicle body posture, complementary filtering is used to realize the data fusion of the gyroscope and the accelerometer, thereby improving the accuracy and stability of the posture measurement result; the control force of the balance vehicle is realized by estimating the peak power output of the battery, and the battery SOC is set as a constraint condition to estimate the SOP, which is more suitable for the evaluation of the control force of the balance vehicle and improves the reliability of the SOP estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 A schematic diagram of the structure of a self-balancing vehicle with automatic adjustment of driving posture provided by an embodiment of the present invention; Figure 2 A schematic diagram of the steps of real-time road surface recognition provided by an embodiment of the present invention; Figure 3 A schematic diagram of the steps for obtaining a test road surface feature set provided in an embodiment of the present invention; Figure 4 A schematic diagram of the steps of calculating the vehicle posture provided by an embodiment of the present invention; Figure 5 A schematic diagram of the steps for establishing a speed-torque fitting curve provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0021] The present invention is based on the current posture control of the balance vehicle, combines posture detection with tire perception detection of the ground, and uses targeted control strategies to improve the adaptability of the balance vehicle to the road surface. At the same time, combined with the judgment of the current state of the balance vehicle, the maximum torque of the balance vehicle against rollover is obtained, and the driving speed limit suitable for the current road surface and with sufficient power to control the posture is found. The posture control is completed and realized, thereby improving the driving safety of the balance vehicle and preventing sudden emergencies.

[0022] Specifically, the balancing vehicle with automatic adjustment of driving posture of the present invention is described in detail through the following contents: This embodiment provides a self-balancing vehicle with automatic adjustment of driving posture. Figure 1As shown, it includes a state detection unit, a posture control unit and a motor drive unit, and the state detection unit, the posture control unit and the motor drive unit are communicatively connected in sequence.

[0023] The state detection unit includes an accelerometer installed on the inner wall of the tire and a posture sensor installed on the vehicle body, which is used to collect wheel rotation state data and vehicle body running posture data, and send the collected data to the posture control unit.

[0024] It should be noted that the wheel rotation state data is collected by the accelerometer on the inner wall of the tire, and the vehicle body running posture data is collected by the posture sensor inside the vehicle body.

[0025] In this embodiment, the accelerometer uses a 3-axis acceleration sensor, which is attached to the inner wall of the tire crown by flexible glue to measure the three-dimensional acceleration signal of the tire, and detects the wheel state to perform real-time road recognition; the attitude sensor includes a gyroscope and an acceleration sensor, which are used to simultaneously measure acceleration and angular velocity. In this embodiment, the attitude sensor uses an MPU6050 sensor, which is a 6-axis motion processing chip with a 3-axis gyroscope and a 3-axis acceleration sensor integrated inside to realize the collection of the attitude and acceleration information of the balance car. The chip itself has a 1024-bit first-in-first-out register to store data from the gyroscope and accelerometer, and to cache serial data. The chip has a built-in DMP (Digital Machine Processor) hardware acceleration engine, and outputs the complete fusion calculation data to the application end through the main IIC interface.

[0026] The posture control unit performs real-time road surface recognition and vehicle posture calculation based on the received data, and outputs a driving posture control signal according to a driving posture control strategy.

[0027] Wherein, the real-time road surface recognition, such as Figure 2 As shown, the following steps are included: S11, data preprocessing: denoising the wheel rotation state data, and smoothing the signal through a smoothing algorithm (such as moving average or low-pass filtering) to reduce the impact of high-frequency noise on subsequent analysis; S12, road surface feature extraction: feature extraction is completed by performing time domain analysis and frequency domain analysis on the data, and a real-time road surface feature set is established based on the selected road surface related features; S13, road surface classification: perform similarity matching between the collected real-time road surface feature set and the test road surface feature set to complete rapid road surface identification, wherein the test road surface feature set is obtained by experimentally testing the time domain and frequency domain response differences of the balance vehicle on the selected road surface.

[0028] Among them, the acquisition of the test road feature set, such as Figure 3 As shown, the following steps are included: S131. Select the required road surface: select the test road surface according to the actual application scenario of the balance vehicle; the test road surface includes sand and gravel, tiles, cement and asphalt roads, etc.

[0029] S132, collecting road surface data: collecting response data of the test road surface through a three-axis acceleration sensor installed inside the tire; S133, data feature extraction: After preprocessing the collected data of each test road surface, characteristic values ​​are obtained through time domain analysis and frequency domain analysis, and features are selected according to the correlation with road surface identification to establish feature sets for different road surfaces.

[0030] It should be noted that time domain analysis includes calculating the basic statistical characteristics of the acceleration signal, such as mean, standard deviation, maximum value, minimum value, kurtosis, zero-crossing rate and signal energy; frequency domain analysis performs fast Fourier transform on the acceleration signal to identify the main frequency components in the signal, including spectrum analysis, main frequency, frequency band energy, frequency center and bandwidth.

[0031] The road surface time domain features selected in this embodiment include mean, standard deviation, maximum, minimum, zero-crossing rate and signal energy, and the frequency domain features include main frequency, frequency band energy and frequency center. The mean reflects the overall level of the signal and can provide basic information about the road surface state; the standard deviation indicates the degree of signal fluctuation and can reveal the degree of road surface unevenness; the maximum and minimum values ​​provide the extreme value range of the signal and can reflect the amplitude of the change in road surface conditions; the zero-crossing rate indicates the frequency at which the signal crosses the zero point and can reflect the friction characteristics and dynamic changes of the road surface; the signal energy is the sum of the energy of the signal and can reflect the interaction intensity between the road surface and the tire; the main frequency is the frequency with the largest energy in the spectrum and can indicate the characteristic frequency of the road surface type; the frequency band energy is the energy within a specific frequency range and can reflect the different characteristics of the road surface conditions (such as smooth, rough, etc.); the frequency center is the center of gravity of the frequency distribution and can provide important information about the road surface characteristics.

[0032] It is understandable that in this embodiment, in order to improve the real-time detection response speed of the road surface of the balancing vehicle during operation, the road surface classification is performed through fast matching, thereby providing a basis for setting the driving parameters. Similarly, to ensure the accuracy of fast identification, the road surface characteristics are extracted in the time domain and frequency domain and then similarity matching is performed.

[0033] The vehicle posture calculation is as follows: Figure 4 As shown, the following steps are included: S21, inclination calculation: calculate the change of inclination through the output of the gyroscope, and obtain the current inclination through integration; calculate the current inclination using the output of the accelerometer; S22, complementary filtering: set the trust weights of the gyroscope and accelerometer, and perform weighted averaging on the measurement results of the two sensors according to the weights to form a fused inclination angle.

[0034] The fusion formula is: ; In the formula, is the inclination angle after fusion, is the inclination angle obtained by the gyroscope, is the inclination angle obtained by the accelerometer, a is the trust weight of the gyroscope measurement, is the angular velocity measured by the gyroscope.

[0035] It is understandable that the above-mentioned vehicle posture calculation realizes the data fusion of the gyroscope and the accelerometer, obtains more accurate and stable posture measurement results, and provides an effective basis for the posture balance control of the balancing vehicle.

[0036] The driving posture control strategy includes: Identify the real-time road surface and detect the peak power (State of Power, SOP) of the self-balancing vehicle battery; The maximum operating speed of the balancing vehicle is obtained through the speed-torque fitting curve according to the road surface recognition results and the battery peak power; It should be noted that the peak power represents the battery's ultimate charge and discharge capacity. In this embodiment, it is mainly used to determine the anti-rollover torque of the balance vehicle's motor by estimating the battery's current SOP, so as to limit the running speed of the balance vehicle. Therefore, in the subsequent SOP estimation, the state of charge (SOC) constraint calculation method is selected to avoid the imbalance of the balance vehicle due to insufficient power and improve the user's driving safety.

[0037] It is understandable that this embodiment uses a motor with a Hall encoder, and the encoder power supply is 5V. This is a Hall encoder with 13-line incremental output. Every time the motor rotates one circle, it outputs 13 pulse signals. The motor encoder has AB phase output. The encoder mode of the microcontroller timer can count the pulses of the two phase signals. Through the phase relationship, up counting and down counting can be realized, so it can not only measure speed, but also identify the direction.

[0038] According to the calculation results of the vehicle posture, the PID algorithm is used to output the control signal of the anti-overturning torque to maintain the balanced posture of the balancing vehicle; Monitor the rotation status of the balance vehicle tires. When a single tire is in a locked state, perform a deceleration turn. When both tires are in a locked state at the same time, calculate and output the control signal of the anti-overturning torque according to the vehicle body posture.

[0039] It should be noted that the locked state described in this embodiment refers to the situation where the tire of the balance vehicle is suddenly blocked from rotating by an obstacle during movement. In this state, the motor needs to output sufficient torque to resist the rollover state, and the locking state of the wheel is determined by the data collected by the accelerometer installed inside the tire. Therefore, in order to improve the safety of the operation of the balance vehicle, the speed of the balance vehicle is limited by identifying the road surface and estimating the peak power output of the battery, thereby ensuring that the balance vehicle has sufficient anti-rollover capability in the event of an emergency.

[0040] It is understandable that for a single wheel locked state, the effect of posture balance control is improved by reducing speed when turning. When both wheels are locked, the torque is output according to the degree of inclination of the vehicle body to resist the overturning state, thereby taking measures to ensure user safety in the first place.

[0041] Among them, the speed-torque fitting curve is established, such as Figure 5 As shown, the following steps are included: S31. Simulate the use process of users with different quality levels and test the maximum anti-rollover moment of each selected required road surface, including road driving and wheel locking test states; S32, selecting a quality grade and a test road surface, obtaining and recording the maximum anti-overturning moment in the test state at different speeds; S33. After multiple tests, the speed-torque fitting curves of users of different quality levels under each road condition are established.

[0042] Specifically, in this embodiment, speed-torque fitting curves of sand and gravel, tile, cement and asphalt pavements are established in turn according to the pavement conditions, with speed as the horizontal axis and anti-overturning moment as the vertical axis, and curves of different quality levels are established in each chart.

[0043] It should be noted that the speed-torque fitting curve obtained through the test is deployed in the driving posture control strategy of the balance vehicle, and the upper limit of the driving speed is obtained according to the current operating scenario (user quality level, road surface recognition and battery peak power), so as to achieve speed limit control and ensure the ability of posture adjustment. In actual applications, the battery peak power obtained through detection also needs to be converted into the maximum torque output by the motor, so as to correspond to the speed-torque fitting curve.

[0044] Obtaining the peak power of the balancing vehicle battery includes the following steps: S41, setting constraint conditions: selecting the state of charge as a constraint condition affecting the safe operation of the battery, and setting the state of charge to be no less than 5%; S42, SOC estimation based on circuit model: Selecting a first-order RC model for SOC estimation, including the following steps: Preliminary parameter identification: The Extended Kalman Filter (EKF) algorithm is used to identify battery parameters online. Through the measurement data of voltage and temperature, EKF can update the parameters of the battery model in real time, laying the foundation for the preliminary estimation of SOC; Preliminary SOC estimation: The EKF algorithm is used to make a preliminary estimate of the SOC. The EKF calculates the current SOC value based on the input and output data of the battery and the first-order RC model. Introducing the Multi-Innovation Theory: In view of the limitation of the Unscented Kalman Filter (UKF) algorithm that only uses the new information at the current moment when updating the state quantity, the Multi-Innovation Theory (MI) is introduced to expand the UKF algorithm so that it can use historical data more effectively and enhance its responsiveness to the current state. MIUKF algorithm execution: In the MIUKF algorithm, the new information matrix and gain matrix are defined, and the extended UKF algorithm is used to update the SOC. Through multiple new information matrices, MIUKF can comprehensively consider the new information at the current moment and the historical moment to update the state; Obtain the final result: Through multiple iterations of the MIUKF algorithm, the final SOC estimation value is formed.

[0045] It is understandable that the MIUKF algorithm overcomes the limitations of traditional UKF in the voltage platform area by introducing multi-innovation theory, realizes accurate SOC estimation under different working conditions, and provides a reliable SOC value for the battery management system.

[0046] S43, SOP estimation based on SOC constraints: Calculate n The change of SOC after a discharge cycle ensures that SOC does not fall below the set minimum limit SOC min , according to the change of SOC, calculate the maximum allowable discharge peak current I max : ; In the formula, C max is the maximum capacity of the battery, SOC k Indicates at time k The state of charge, SOCmin Indicates the battery's lowest state of charge. n is the number of discharge cycles, T s is the length of the sampling period.

[0047] Finally, the estimated value of SOC is calculated by terminal voltage and peak current.

[0048] It should be noted that in order to ensure that the battery can provide the motor with sufficient output power to generate sufficient torque in an emergency, it is necessary to monitor the peak power of the battery so as to adjust the speed of the balance vehicle under different road conditions in real time.

[0049] This embodiment is based on the first-order RC model, adopts the EKF algorithm to identify circuit parameters online, introduces the multi-innovation theory to improve the limitations of the UKF algorithm, and finally estimates the SOP based on the online parameter identification and SOC estimation, considering the limitation of SOC.

[0050] The motor drive unit drives the motor according to the driving posture control signal, so that the balancing vehicle maintains a balanced posture and continues to run. The motor drive unit realizes the direction and speed control of the motor through the motor drive circuit.

[0051] In this embodiment, the motor drive circuit adopts the BTN7960 dual-channel full-bridge high-current driver chip, whose power supply voltage range is 5.5V~28V and the operating temperature is -40℃~150℃. The chip has a wide operating voltage range and the operating voltage can be configured according to actual needs. It can control two external MOSFET tubes (one controls the direction of the motor and the other controls the speed of the motor), and control the direction and speed of the motor by controlling these two external MOSFET tubes. At the same time, optocoupler isolation is used to protect the single-chip microcomputer from being damaged by the large current of the power part. The motor drive circuit consists of two optocoupler chips and four motors. The single-chip microcomputer controls the two motors by outputting 4-way PWM to realize the forward and reverse rotation of the motor.

[0052] The present invention realizes the adjustment of the posture of the balance vehicle through two parts of detection, including driving road surface recognition and vehicle body posture detection. According to the vehicle body posture calculation result, the PID algorithm is used to output the control signal of the anti-overturning torque to maintain the balanced posture of the balance vehicle body. At the same time, the driving speed is limited according to the speed-torque fitting curve according to the conditions of the operating scene to ensure that the control force of the balance vehicle posture adjustment is sufficient, thereby improving the operating safety of the balance vehicle and ensuring that the balance vehicle can sufficiently adjust the overturning state in the emergency situation of tire locking.

[0053] In the process of road surface recognition, the present invention extracts features based on data collected by an acceleration sensor installed inside a tire, and quickly classifies the real-time road surface in combination with a test road surface feature set, thereby providing a basis for speed limit control, and at the same time improving the recognition response speed through similarity classification; in the calculation of the vehicle body posture, complementary filtering is used to realize the data fusion of the gyroscope and the accelerometer, thereby improving the accuracy and stability of the posture measurement result; the control force of the balance vehicle is realized by estimating the peak power output of the battery, and the battery SOC is set as a constraint condition to estimate the SOP, which is more suitable for the evaluation of the control force of the balance vehicle and improves the reliability of the SOP estimation.

[0054] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A self-balancing vehicle with automatic driving posture adjustment, characterized in that: It includes a state detection unit, a posture control unit and a motor drive unit, wherein the state detection unit, the posture control unit and the motor drive unit are sequentially connected in communication; The posture control unit performs real-time road surface recognition and vehicle posture calculation based on the received data, and outputs a driving posture control signal according to the driving posture control strategy; Wherein, the driving posture control strategy includes: Identify the real-time road surface and detect the peak power of the balancing vehicle battery; The maximum operating speed of the balancing vehicle is obtained through the speed-torque fitting curve according to the road surface recognition results and the battery peak power; According to the calculation results of the vehicle posture, the PID algorithm is used to output the control signal of the anti-overturning torque to maintain the balanced posture of the balancing vehicle; Monitor the rotation status of the balance vehicle's tires. When a single tire is in a locked state, perform a deceleration turn. When both tires are in a locked state at the same time, calculate and output the control signal of the anti-overturning torque according to the vehicle body posture.

2. A self-balancing vehicle with automatic driving posture adjustment according to claim 1, characterized in that: The real-time road surface recognition comprises the following steps: Data preprocessing: De-noise the wheel rotation state data and smooth the signal using a smoothing algorithm; Pavement feature extraction: Feature extraction is completed by performing time domain analysis and frequency domain analysis on the data, and a real-time pavement feature set is established based on the selected pavement-related features; Road surface classification: The road surface is quickly identified by performing similarity matching between the collected real-time road surface feature set and the test road surface feature set. The test road surface feature set is obtained by experimentally testing the time domain and frequency domain response differences of the balance vehicle on the selected road surface.

3. A self-balancing vehicle with automatic driving posture adjustment according to claim 2, characterized in that: The acquisition of the test road surface feature set includes the following steps: Select the required road surface: select the test road surface according to the actual application scenario of the balance vehicle; the test road surface includes sand, gravel, tile, cement and asphalt road surface; Collecting road surface data: The three-axis acceleration sensor installed inside the tire collects the response data of the test road surface; Data feature extraction: After preprocessing the collected data of each test road surface, the characteristic values ​​are obtained through time domain analysis and frequency domain analysis, and the features are selected according to the correlation with road surface identification to establish the feature sets of different road surfaces; The selected road surface time domain features include mean, standard deviation, maximum, minimum, zero-crossing rate and signal energy, and the frequency domain features include main frequency, frequency band energy and frequency center.

4. A self-balancing vehicle with automatic driving posture adjustment according to claim 1, characterized in that: The vehicle posture calculation comprises the following steps: Inclination calculation: Calculate the change of inclination angle through the output of the gyroscope, and get the current inclination angle through integration; Calculate the current inclination angle using the output of the accelerometer; Complementary filtering: Set the trust weights of the gyroscope and accelerometer, and perform weighted averaging on the measurement results of the two sensors according to the weights to form a fused inclination angle; The fusion formula is: ； In the formula, is the inclination angle after fusion, is the inclination angle obtained by the gyroscope, is the inclination angle obtained by the accelerometer, a is the trust weight of the gyroscope measurement, is the angular velocity measured by the gyroscope.

5. The self-balancing vehicle with automatic driving posture adjustment according to claim 1, characterized in that: The establishment of the speed-torque fitting curve comprises the following steps: Simulate the use process of users with different quality levels and test the maximum anti-rollover moment of each selected required road surface, including road driving and wheel locking test states; Select the quality grade and test road surface, obtain the maximum anti-overturning moment in the test state at different speeds and record it; After multiple tests, the speed-torque fitting curves for users of different quality levels under each road condition were established.

6. The self-balancing vehicle with automatic driving posture adjustment according to claim 1, characterized in that: The method of detecting the peak power of the battery of the self-balancing vehicle comprises the following steps: Set constraint conditions: Select the state of charge as the constraint condition that affects the safe operation of the battery, and set the state of charge to no less than 5%; SOC estimation based on circuit model: Select the first-order RC model for SOC estimation, including the following steps: Preliminary parameter identification: Use the extended Kalman filter algorithm to identify battery parameters online; Preliminary SOC estimation: The EKF algorithm is used to make a preliminary estimate of the SOC. The EKF calculates the current SOC value based on the input and output data of the battery and the first-order RC model. Introducing multiple innovation theory: Introducing multiple innovation theory to expand the unscented Kalman filter algorithm; MIUKF algorithm execution: define the innovation matrix and gain matrix, and use the extended unscented Kalman filter algorithm to update the SOC; Obtain the final result: Through multiple iterations of the MIUKF algorithm, the final SOC estimation value is formed; SOP estimation based on SOC constraints: Calculate n The change of SOC after a discharge cycle ensures that SOC does not fall below the set minimum limit SOC min , according to the change of SOC, calculate the maximum allowable discharge peak current.

7. A self-balancing vehicle with automatic adjustment of driving posture according to claim 6, characterized in that: The maximum discharge peak current I max , the calculation formula is: ； In the formula, C max is the maximum capacity of the battery, SOC k Indicates at time k The state of charge, SOC min Indicates the battery's lowest state of charge. n is the number of discharge cycles, T s is the length of the sampling period.

8. The self-balancing vehicle with automatic driving posture adjustment according to claim 1, characterized in that: The state detection unit includes an accelerometer installed on the inner wall of the tire and a posture sensor installed on the vehicle body, which is used to collect wheel rotation state data and vehicle body running posture data, and send the collected data to the posture control unit.

9. A self-balancing vehicle with automatic driving posture adjustment according to claim 8, characterized in that: The wheel rotation state data is collected by an accelerometer on the inner wall of the tire, and the vehicle body running posture data is collected by a posture sensor inside the vehicle body; The attitude sensor includes a gyroscope and an acceleration sensor, which are used to measure acceleration and angular velocity simultaneously.

10. The self-balancing vehicle with automatic driving posture adjustment according to claim 1, characterized in that: The motor drive unit drives the motor according to the driving posture control signal, so that the self-balancing vehicle maintains a balanced posture and continues to run; The motor drive unit realizes the direction and speed control of the motor through the motor drive circuit, wherein the motor drive circuit adopts a current drive chip to control the direction and speed of the motor.

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