Electric unmanned vehicle ramp and vehicle weight estimation system based on IMU (Inertial Measurement Unit) signal

Abstract

The invention relates to an electric unmanned vehicle ramp and vehicle weight estimation system based on IMU signals. The system comprises a vehicle control unit VCU, a motor control unit MCU, an inertial measurement unit IMU and a wheel speed sensor. An acceleration signal of a vehicle is obtained through an inertial measurement unit (IMU), a motor torque signal is provided by an MCU, a vehicle speed signal is calculated by a wheel speed sensor, signals of a vehicle control unit (VCU), a motor control unit MCU, the IMU and the wheel speed sensor are calculated, and a ramp angle and a vehicle weight estimated value are calculated by utilizing a specific algorithm. The method can effectively separate the vehicle mass and gradient signals, improve the estimation precision, reduce the dependence on the precision of a vehicle dynamic model, enhance the adaptability and reliability of the system in various environments, and provide an important basis for the dynamic control and energy management of the electric unmanned vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of control of electric unmanned vehicles, and in particular to a ramp and vehicle weight estimation system for electric unmanned vehicles based on IMU signals. Background Art

[0002] With the rapid development of autonomous driving technology, vehicle positioning and state estimation have become one of the key technologies. In the field of electric unmanned vehicles, accurate estimation of slope and vehicle weight is crucial for vehicle dynamic control and energy management. At present, the estimation of vehicle mass and road slope is mainly based on the method of vehicle longitudinal dynamics, which uses the longitudinal dynamics model of the vehicle combined with the data on the vehicle CAN bus to estimate the unknown system parameters. However, the existing methods have some defects: the vehicle mass and slope signals are coupled, making it difficult to accurately separate these two variables; they are highly dependent on the accuracy of the vehicle model, and the accuracy of the estimation results will be affected when the model is inaccurate; the time-varying nature of the road also increases the complexity of the estimation process. Summary of the invention

[0003] In order to solve the above problems, the present invention provides an electric unmanned vehicle slope and vehicle weight estimation system based on IMU signals, which aims to improve the estimation accuracy of the slope and vehicle weight of the electric unmanned vehicle during driving and enhance the vehicle's dynamic control and energy management capabilities. The technical solution is as follows:

[0004] An electric unmanned vehicle slope and vehicle weight estimation system based on IMU signals, comprising:

[0005] Vehicle Control Unit (VCU);

[0006] Motor controller MCU (Motor Control Unit);

[0007] Inertial measurement unit IMU, used to send linear acceleration signals to the CAN communication bus;

[0008] Wheel speed sensor, used to send pulse signals to EHB (Electronic Hydraulic Braking), EHB calculates the wheel speed and sends the vehicle speed signal through the CAN communication bus;

[0009] The vehicle controller VCU is used to receive the acceleration signal from the IMU, the motor torque signal from the MCU and the vehicle speed signal from the EHB, obtain the ramp and vehicle weight signals through calculation and send them to the CAN bus.

[0010] Furthermore, the inertial measurement unit IMU includes an accelerometer for detecting acceleration signals of the object in three independent axes of the carrier coordinate system: X-axis linear acceleration, Y-axis linear acceleration, and Z-axis linear acceleration.

[0011] Furthermore, the vehicle controller VCU calculates the ramp angle through the following steps:

[0012] Get the acceleration data on the X-axis from the IMU;

[0013] Remove the influence of disturbances such as vehicle motion acceleration to obtain the component of gravity acceleration in the ramp direction;

[0014] The ramp value is obtained through mathematical operations.

[0015] Furthermore, the vehicle controller VCU estimates the vehicle weight through the following steps:

[0016] Obtaining the slope value, vehicle speed, vehicle acceleration, and drive motor torque calculated by the ramp estimation module as input;

[0017] The vehicle longitudinal dynamics model is converted into the least squares format to obtain the system output, observable data vector, and system parameters to be identified;

[0018] The vehicle weight estimate is calculated using the recursive least squares method.

[0019] Furthermore, the signal processing module is used to perform anti-jitter processing on the input signal and filter out noise in the signal.

[0020] Furthermore, the vehicle controller VCU is also used to use the estimated slope and vehicle weight signals for adjusting the vehicle's driving strategy, including motor output torque adjustment and driving speed control.

[0021] The advantages or beneficial effects of the above technical solution include:

[0022] (1) Through real-time monitoring and data processing of the inertial measurement unit (IMU) signal, the slope and weight of the electric unmanned vehicle can be accurately estimated, which is more accurate than the traditional method based on the vehicle dynamics model.

[0023] (2) The inertial measurement unit (IMU) is easy to integrate and can capture the dynamic changes of the vehicle in real time, thereby achieving fast and accurate slope and vehicle weight estimation, reducing dependence on external signals such as GPS, and enhancing the system's adaptability and reliability in various environments, especially maintaining performance in complex or harsh environments.

[0024] (3) Through accurate slope and vehicle weight estimation, the vehicle can better adjust its driving strategy, optimize energy consumption, and improve energy utilization efficiency.

[0025] (4) Adjust the vehicle's suspension and braking systems based on vehicle weight information to improve the vehicle's stability and safety under different load conditions and reduce the risk of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0027] Figure 1 This is an architecture diagram of an electric unmanned vehicle slope and vehicle weight estimation system based on IMU signals;

[0028] Figure 2 It is the vehicle speed signal feedback logic diagram;

[0029] Figure 3 This is a schematic diagram of the acceleration signal feedback of the inertial measurement unit IMU;

[0030] Figure 4 This is a schematic diagram of motor torque signal feedback;

[0031] Figure 5 The architecture diagram of the slope estimation system for electric driverless vehicles;

[0032] Figure 6 This is the architecture diagram of the electric unmanned vehicle weight estimation system. DETAILED DESCRIPTION

[0033] In the following, only some exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be illustrative and non-restrictive in nature.

[0034] like Figure 1 As shown, an electric unmanned vehicle slope and vehicle weight estimation system based on IMU signals includes a vehicle controller VCU (Vehicle Control Unit), a motor controller MCU (Motor Control Unit), and an inertial measurement unit IMU. The inertial measurement unit IMU sends a linear acceleration signal to a CAN communication bus, the motor controller MCU sends a motor torque signal to the CAN communication bus, and the wheel speed sensor sends a pulse signal to the EHB (Electronic Hydraulic Braking). The wheel speed sensor EHB calculates the wheel speed and sends the vehicle speed signal through the CAN communication bus. After calculation by the VCU vehicle control unit, the slope and vehicle weight signals are obtained and sent to the CAN bus.

[0035] The vehicle controller VCU (Vehicle Control Unit) uses Jiyu Technology's VCU-V31, which has powerful data processing capabilities and rich communication interfaces. It supports multiple bus protocols, including CAN, LIN, etc., and can achieve precise control and coordinated management of various vehicle subsystems. It is suitable for various types of electric vehicles.

[0036] The motor controller MCU (Motor Control Unit) uses Jiyu Technology's MCU-800, which adopts advanced vector control algorithm and has high efficiency, high precision and high reliability. It supports a variety of motor types, including permanent magnet synchronous motors, asynchronous motors, etc. It can accurately control the speed and torque of the motor according to the real-time status and load requirements of the motor, and send the motor torque signal to the VCU through the CAN bus.

[0037] The inertial measurement unit (IMU) uses the Xsens MTi 300 series, which integrates high-precision accelerometers, gyroscopes, and magnetometers, and can measure the acceleration, angular velocity, and magnetic field information of the vehicle in three axes in real time. Its built-in algorithm can effectively filter out noise and interference, provide a stable linear acceleration signal, and send the signal to the VCU via the CAN bus, providing accurate data support for slope and vehicle weight estimation.

[0038] The wheel speed sensor uses Continental's ABS 8.0. The wheel speed sensor uses the Hall effect principle and can accurately detect the speed and direction of the wheel. It has the characteristics of high precision, high stability and strong anti-interference ability. The output pulse signal can be directly sent to the EHB (Electronic Hydraulic Braking) system to calculate the wheel speed and realize anti-lock braking control.

[0039] The signal processing module in the system includes input signal processing and output signal processing. For input digital signals, the signal processing module needs to perform anti-jitter processing on the signal to filter out the noise in the signal. For analog signals, it detects whether the input signal is within the reasonable range of the signal. If it exceeds the reasonable upper and lower limits of the signal, the signal is deemed invalid. For CAN communication, the software will determine whether the signal reception has timed out and lost frames based on signals such as rolling counts, and convert the original signal into an actual physical signal according to the agreed communication protocol. In the signal processing module, the CAN input signal will be anti-jittered to avoid interference caused by frame loss and signal errors. Before the output signal is output to the CAN network, the software will detect the upper and lower limits of the output signal and has an overwrite and rewrite function.

[0040] like Figure 2As shown, during the driving process, the wheel speed sensor collects the wheel speed data of the vehicle. The data fed back by the left front wheel speed sensor of the vehicle and the data fed back by the right front wheel speed sensor of the vehicle are added, and the sum is divided by 2 to obtain the current actual speed of the vehicle.

[0041] like Figure 3 As shown, the inertial measurement unit IMU relies on the accelerometer in its own measurement unit to detect the acceleration signals of the object in the three independent axes of the carrier coordinate system - X-axis linear acceleration, Y-axis linear acceleration, and Z-axis linear acceleration.

[0042] like Figure 4 As shown in the figure, the torque of the motor is judged by observing the acceleration state of the vehicle. When accelerating, the motor outputs a large torque; when the vehicle decelerates or stops, the motor outputs a small torque. A torque sensor is used to sense the output torque of the motor, and the output end of the torque sensor is connected to the input end of the control circuit to receive the torque signal, and these signals are used for logical judgment in the program.

[0043] The electric unmanned vehicle ramp system based on IMU signal is as follows Figure 5 As shown, by obtaining the acceleration data on the X-axis from the inertial measurement unit IMU, in order to obtain the component of gravity acceleration in the ramp direction, it is necessary to remove the influence of interference such as vehicle motion acceleration, subtract the vehicle motion acceleration from the acceleration on the X-axis, and finally calculate the gravity component and obtain the ramp value through mathematical operations. The specific calculation formula is as follows:

[0044] When a vehicle is traveling on a slope, the acceleration measured by the inertial measurement unit (IMU) can be decomposed into the actual motion acceleration of the vehicle and the gravity acceleration component caused by the slope, as shown in formula (1). By subtracting the vehicle acceleration and other interference quantities, an acceleration value mainly related to the slope can be obtained. This value actually includes the component of gravity acceleration in the direction of the slope. According to Newton's second law, the component of gravity acceleration in the direction of the slope can be expressed as, where is the gravity acceleration (approximately equal to 9.8m / s 2 ), is the angle of the ramp. Therefore, by dividing the obtained acceleration value by 9.8, the sine value of the slope can be obtained, that is, the sine value of the slope can be converted into an angle expression by the inverse sine function (arcsin), as shown in formula (2).

[0045]

[0046] Where: a x is the X-axis acceleration sent by IMU, is the actual acceleration of the vehicle.

[0047] Electric unmanned vehicle weight estimation system Figure 6As shown in the figure, the vehicle weight is estimated by taking the slope value, vehicle speed, vehicle acceleration and drive motor torque calculated by the ramp estimation module as inputs, converting the vehicle longitudinal dynamics model into the least squares format, and obtaining the system output, observable data vector and system parameters to be identified. Finally, the vehicle weight estimation is calculated by the recursive least squares method. The vehicle longitudinal dynamics equation is:

[0048] F t =F w +F f +F i +F j (3)

[0049] Where: F t is the vehicle driving force, F w is the air resistance, F f is the rolling resistance, F i is the road slope resistance, F j For acceleration resistance.

[0050] The driving force in the kinetic equation is:

[0051]

[0052] Where: T is the driving motor torque, i g is the transmission ratio of the transmission. i0 is the transmission ratio of the main reducer, η is the mechanical efficiency of the transmission system, and r is the wheel radius (m) and the air resistance is:

[0053]

[0054] Where: C D is the air resistance coefficient, is the vehicle speed (km / h). A is the frontal area (m 2 ).

[0055] The rolling resistance is

[0056] F f = mgf (5)

[0057] Where: f is the rolling resistance coefficient, m is the vehicle weight (kg), g is the acceleration due to gravity (m / s 2 ).

[0058] The slope resistance is:

[0059] F i = mgi (6)

[0060] Where: i is the road slope, m is the vehicle weight (kg), g is the acceleration due to gravity (m / s 2 ).

[0061] The acceleration resistance is:

[0062] F j =δma (7)

[0063] Where: δ is the vehicle rotation mass conversion coefficient, a is the vehicle acceleration (m 2 ).

[0064] The expression is as follows:

[0065]

[0066] Where: I ω is the moment of inertia of the wheel, its unit is (kg·m 2 ), I f is the moment of inertia of the flywheel, in kg·m 2 ).

[0067] Transforming the vehicle longitudinal dynamics formula into the least squares formula, we can get:

[0068]

[0069] Where: F tw is the vehicle torque output, a_e is the observable data vector, θ is the system parameter to be identified, and e is the process white noise.

[0070] The identification quality at the k-1th moment and the kth moment is defined as The system output and observable data vector at the kth moment are F tw (k), a_e(k). Then, according to the recursive least squares method, the quality identification least squares recursive format can be obtained as follows:

[0071]

[0072] γ(k)=P(k-1)a_e(k)[a_e(k)P(k-1)a_e(k))+μ(k)] -1

[0073]

[0074] In formula (10), μ(k) is the forgetting factor at the kth moment, 0<=μ(k)<=1.

[0075] When a vehicle is driving on the road, the system output F(tw) and the observable data vector a_e are calculated, and the mass is identified using the least squares method. The result is an estimate of the current vehicle weight.

[0076] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. Therefore, the scope of protection of the present application shall be based on the scope of protection of the claims.

Claims

1. An electric unmanned vehicle ramp and vehicle weight estimation system based on IMU signals, characterized in that: include: Vehicle controller VCU; Motor controller MCU; Inertial measurement unit IMU, used to send linear acceleration signals to the CAN communication bus; Wheel speed sensor, used to send pulse signals to EHB, EHB calculates the wheel speed and sends the vehicle speed signal through the CAN communication bus; The vehicle controller VCU is used to receive the acceleration signal from the IMU, the motor torque signal from the motor controller MCU and the vehicle speed signal from the EHB, obtain the ramp and vehicle weight signals through calculation and send them to the CAN bus.

2. The electric unmanned vehicle ramp and vehicle weight estimation system based on IMU signals according to claim 1, characterized in that: The inertial measurement unit IMU includes an accelerometer for detecting acceleration signals of an object in three independent axes of a carrier coordinate system: X-axis linear acceleration, Y-axis linear acceleration, and Z-axis linear acceleration.

3. The electric unmanned vehicle ramp and vehicle weight estimation system based on IMU signals according to claim 1, characterized in that: The vehicle controller VCU calculates the ramp angle by the following steps: Get the acceleration data on the X-axis from the inertial measurement unit IMU; The influence of the vehicle motion acceleration disturbance is removed to obtain the component of gravity acceleration in the ramp direction; The ramp value is obtained through mathematical operations.

4. The electric unmanned vehicle ramp and vehicle weight estimation system based on IMU signals according to claim 1, characterized in that: The vehicle controller VCU estimates the vehicle weight by the following steps: Obtaining the slope value calculated by the ramp estimation module, the vehicle speed, the vehicle acceleration, and the drive motor torque as input; The vehicle longitudinal dynamics model is converted into the least squares format to obtain the system output, observable data vector, and system parameters to be identified; The vehicle weight estimate is calculated using the recursive least squares method.

5. The electric unmanned vehicle ramp and vehicle weight estimation system based on IMU signals according to claim 1, characterized in that: It also includes a signal processing module for performing anti-jitter processing on the input signal, filtering out noise in the signal, and converting the original signal into an actual physical signal.

6. The electric unmanned vehicle ramp and vehicle weight estimation system based on IMU signals according to claim 1, characterized in that: The vehicle controller VCU is also used to use the estimated slope and vehicle weight signals for adjusting the vehicle's driving strategy, including motor output torque adjustment and driving speed control.

7. A method for estimating the slope and weight of an electric unmanned vehicle based on an IMU signal, using the system for estimating the slope and weight of an electric unmanned vehicle based on an IMU signal according to any one of claims 1 to 6, characterized in that: The following steps are involved: Obtain the vehicle's acceleration signal through the inertial measurement unit IMU; Get the motor torque signal from the motor controller MCU; Obtain vehicle speed signal from wheel speed sensor; According to the acquired acceleration signal, motor torque signal and vehicle speed signal, the ramp and vehicle weight signals are calculated.

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