Multi-environment engineering vehicle speed estimation method and system

Abstract

The invention discloses a multi-environment engineering vehicle speed estimation method and system, and belongs to the technical field of engineering vehicle control. The method comprises the following steps: acquiring a vehicle wheel speed, a vehicle body attitude, a GNSS original signal and a demand torque signal; performing kinematic correction on the wheel speed based on the vehicle body posture signal, and selecting an optimal wheel speed signal according to the required torque; calculating an optimal wheel acceleration through a tracking differentiator, comparing the optimal wheel acceleration with a longitudinal acceleration of a vehicle body, and judging single-wheel and full-wheel slip states; selecting a target mode from three predefined modes according to the slip state in combination with the effectiveness of the GNSS signal; and performing fusion estimation on the optimal wheel speed signal, the vehicle body longitudinal acceleration and the GNSS speed signal to obtain a vehicle speed estimation value. Through adaptive multi-mode fusion and multi-level signal compensation, the precision and robustness of vehicle speed estimation in the complex operation environment are improved, and the method is used for real-time vehicle speed estimation of the hinged distributed electric drive engineering vehicle in the complex operation environment.

Description

Technical Field

[0001] This invention relates to the field of engineering vehicle control technology, specifically to a method and system for estimating the speed of engineering vehicles in various environments. Background Technology

[0002] Articulated distributed electric drive engineering vehicles, such as loaders, offer advantages in flexibility, efficiency, and energy saving, representing a significant future development direction for construction machinery. In diverse operational environments such as digging, transporting, and unloading, the vehicle's motion is extremely complex. Particularly in digging operations, the interaction between the bucket and the material causes drastic changes in the vehicle's longitudinal acceleration, pitch angle, and vertical loads on the front and rear axles, easily leading to drive wheel slippage and reduced traction, severely impacting operational efficiency and continuity. Therefore, accurate and real-time longitudinal speed estimation is a crucial input benchmark for advanced control functions such as vehicle drive anti-slip control and power coordination control, and is essential for ensuring the vehicle's operational performance and safety under complex and variable conditions.

[0003] Currently, vehicle speed estimation methods for engineering vehicles mainly rely on wheel speed sensors. For example, existing technology discloses a longitudinal reference speed estimation method and system applicable to multiple working conditions, and a vehicle (patent publication number CN119239618A). This method selects different wheel speeds as reference speeds by detecting different working conditions such as low speed, braking, and turning. However, this method is highly dependent on the accuracy of sensor signals and the reliability of working condition identification logic. When sensors are affected by noise interference or multiple working condition features overlap, misjudgments are prone to occur, leading to a decrease in vehicle speed estimation accuracy. In addition, its turning condition calculation involves multiple sensors, which limits its application in cost-sensitive situations.

[0004] Another existing technology proposes a method and device for estimating the longitudinal speed of all-wheel distributed vehicles (patent publication number CN117622165A). This method assesses reliability by comparing predicted and measured wheel speed / acceleration values, and obtains the vehicle speed based on data fusion from reliable wheels. While this method improves data reliability to some extent, it still relies on a single kinematic framework. In the event of all-wheel slippage or short-term sensor anomalies, the reliability assessment mechanism may fail, leading to significant fluctuations or delays in vehicle speed estimation.

[0005] In summary, existing methods for estimating the speed of engineering vehicles generally suffer from unstable estimation accuracy and insufficient robustness when facing multiple environmental challenges in actual operations, such as uneven road surface adhesion, large load impacts, and frequent changes in operating conditions. They often rely on a single information source or simple rules, resulting in poor adaptability to dynamically changing environments and operating conditions. Furthermore, in embedded control systems with high real-time requirements, it is difficult to achieve a balance between accuracy and computational efficiency. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for estimating the speed of engineering vehicles in multiple environments, so as to overcome the above-mentioned problems existing in the prior art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for estimating the speed of engineering vehicles in various environments includes the following steps: The vehicle acquires wheel speed signals for each wheel, vehicle attitude signals, raw GNSS signals from the Global Navigation Satellite System, and demand torque signals characterizing the vehicle's operating state. The vehicle attitude signals are obtained by measuring the vehicle's attitude using an inertial measurement unit (IMU). The wheel speed signal is kinematically calibrated based on the vehicle body posture signal, and the optimal wheel speed signal is selected from the calibrated wheel speed signals of each wheel according to the required torque signal. Based on the optimal wheel speed signal, the optimal wheel acceleration is calculated by a tracking differentiator, and the optimal wheel acceleration is compared with the vehicle longitudinal acceleration. Based on the comparison result and combined with a preset threshold condition, the single-wheel slip state and the all-wheel slip state of the vehicle are determined, wherein the vehicle longitudinal acceleration is calculated based on the vehicle attitude signal. Based on the determined slip state and the validity of the raw GNSS signal, a corresponding target mode is selected from a variety of predefined vehicle speed fusion estimation modes; the vehicle speed fusion estimation modes include pure wheel speed mode, wheel speed and IMU fusion mode, and GNSS and IMU fusion mode; Based on the target mode, the optimal wheel speed signal and the vehicle longitudinal acceleration are fused and estimated to obtain the vehicle longitudinal speed estimate at the current moment; wherein when the target mode is the GNSS and IMU fusion mode, the fusion estimation also includes the GNSS speed signal obtained after delay correction of the original GNSS signal.

[0008] Furthermore, the motion correction specifically involves: using the yaw rate in the vehicle body posture signal to compensate the wheel speed signal of each wheel, eliminating the influence of wheel speed differences during vehicle steering on the selection of the optimal wheel speed signal, and obtaining the corrected wheel speed signal of each wheel.

[0009] Furthermore, the selection of the optimal wheel speed signal specifically involves: When the value of the required torque signal is positive, the wheel with the smallest wheel speed signal value is selected from the corrected wheel speed signals of each wheel as the optimal wheel, and its wheel speed signal is the optimal wheel speed signal. When the value of the required torque signal is negative, the wheel with the largest wheel speed signal value is selected from the corrected wheel speed signals of each wheel as the optimal wheel, and its wheel speed signal is the optimal wheel speed signal.

[0010] Furthermore, the determination of the single-wheel slippage state includes: If the difference between the optimal wheel acceleration and the longitudinal acceleration of the vehicle body is greater than or equal to the first threshold, then it is determined that a single wheel slip has occurred. If the difference between the optimal wheel acceleration and the longitudinal acceleration of the vehicle body is less than the first threshold, and the duration of the difference exceeds the second threshold after the preset conditions are met, then the single-wheel slippage state is determined to end; wherein the preset conditions include the value of the demand torque signal being less than the third threshold or the value of the optimal wheel speed signal being greater than the fourth threshold.

[0011] Furthermore, the determination of the all-wheel slippage state includes: if all wheels are determined to have single-wheel slippage, then all-wheel slippage is determined to have occurred.

[0012] Furthermore, the selection logic for the various predefined vehicle speed fusion estimation modes includes: When neither single-wheel slippage nor full-wheel slippage occurs, select the pure wheel speed mode; When a single wheel slip occurs but the entire wheel does not slip, select the wheel speed and IMU fusion mode; When full wheel slip occurs and the original GNSS signal is valid, the GNSS and IMU fusion mode is selected; When full wheel slip occurs and the original GNSS signal fails, select the wheel speed and IMU fusion mode and increase the fusion weight of the IMU signal.

[0013] Furthermore, the fusion estimation is implemented using the Kalman filter algorithm.

[0014] Furthermore, the longitudinal acceleration of the vehicle body is calculated based on the vehicle body attitude signal. Specifically, based on the pitch angle in the vehicle body attitude signal and combined with the installation angle error of the IMU, the original longitudinal acceleration of the vehicle body in the vehicle body attitude signal is compensated for by gravity component and coordinate projection to obtain the calculated longitudinal acceleration of the vehicle body.

[0015] Furthermore, the original GNSS signal is delayed and corrected, specifically by performing time delay compensation on the original GNSS signal based on the longitudinal acceleration of the vehicle body, to obtain the delayed-corrected GNSS velocity signal.

[0016] Another objective of this invention is to provide a multi-environment engineering vehicle speed estimation system, wherein the system, when executed, implements the aforementioned multi-environment engineering vehicle speed estimation method, comprising: The signal acquisition module is configured to acquire wheel speed signals of each wheel of the vehicle, vehicle attitude signals, raw GNSS signals from the Global Navigation Satellite System, and demand torque signals characterizing the vehicle's operating state, wherein the vehicle attitude signals are obtained by measuring the inertial measurement unit (IMU). The wheel speed processing module is configured to perform motion correction on the wheel speed signal based on the vehicle body posture signal, and select the optimal wheel speed signal from the corrected wheel speed signals of each wheel according to the required torque signal; The slippage detection module is configured to calculate the optimal wheel acceleration based on the optimal wheel speed signal using a tracking differentiator, compare the optimal wheel acceleration with the vehicle body longitudinal acceleration, and determine the single-wheel slippage state and all-wheel slippage state of the vehicle based on the comparison result, wherein the vehicle body longitudinal acceleration is calculated based on the vehicle body attitude signal; The mode selection module is configured to select a corresponding target mode from a variety of predefined vehicle speed fusion estimation modes based on the determined slip state and the validity of the raw GNSS signal; the vehicle speed fusion estimation modes include pure wheel speed mode, wheel speed and IMU fusion mode, and GNSS and IMU fusion mode; The speed fusion module is configured to perform a fusion estimation of the optimal wheel speed signal and the vehicle longitudinal acceleration based on the target mode to obtain the vehicle longitudinal speed estimate at the current moment; wherein when the target mode is the GNSS and IMU fusion mode, the speed fusion module is further configured to perform delay correction on the original GNSS signal to obtain a GNSS speed signal, and incorporate the GNSS speed signal into the fusion estimation.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention introduces a multi-mode fusion architecture based on slip trend judgment. By accurately calculating wheel acceleration through a tracking differentiator and comparing it with IMU acceleration, it achieves accurate and rapid identification of single-wheel and all-wheel slip states. For different slip states (e.g., normal, single-wheel slip, all-wheel slip) and sensor conditions (e.g., GNSS active / inactive), it adaptively switches to the optimal fusion estimation mode (e.g., pure wheel speed, wheel speed + IMU, GNSS + IMU). This multi-mode decision-making mechanism ensures that even under adverse conditions such as tire slippage and sudden changes in adhesion coefficient, the system can always call upon relatively reliable information sources for fusion, thereby significantly improving the accuracy and robustness of vehicle speed estimation in variable environments.

[0018] 2. Before information fusion, this invention implements multi-level signal preprocessing and compensation. For the steering characteristics of articulated vehicles, motion correction is performed on the original wheel speeds to eliminate wheel speed difference errors caused by steering. The optimal wheel speed is intelligently selected based on the vehicle's required torque, providing initial wheel speed information closer to the actual vehicle speed for subsequent processing. Gravity component and installation error compensation are applied to the IMU acceleration, resulting in more accurate longitudinal vehicle acceleration. To address the inherent delay of GNSS signals, look-ahead compensation is performed using IMU acceleration integration, effectively overcoming signal lag issues. These processes significantly improve the accuracy and availability of each information source, creating the necessary conditions for high-precision multi-source information fusion.

[0019] 3. Enhanced system survivability under extreme conditions: Even in extreme conditions such as severe all-wheel slippage and GNSS signal loss, this invention can still estimate vehicle speed using a wheel speed + IMU mode with increased IMU fusion weights, avoiding the loss of estimation function caused by the failure of core information sources in traditional methods. These features demonstrate the system's strong fault tolerance and full-condition coverage, providing a reliable vehicle speed reference for the continuous, safe, and efficient operation of engineering vehicles in extremely harsh working environments. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the overall technical solution of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] This invention provides a method and system for estimating the speed of engineering vehicles in various environments. Figure 1 This is an overall flowchart of the technical solution of the present invention. The purpose is to solve the technical problems of inaccurate longitudinal speed estimation and poor robustness of articulated distributed electric drive engineering vehicles in complex excavation, slope operation and other variable environments, due to tire slippage, load impact and sensor signal interference.

[0023] This embodiment provides a method for estimating vehicle speed in multi-environment engineering vehicles. This method, based on multi-source information fusion and adaptive decision-making, mainly includes: signal acquisition and preprocessing, intelligent identification of slip states, multi-mode adaptive decision-making, and vehicle speed fusion estimation. The method corrects wheel speeds using kinematic principles and intelligently selects the optimal wheel speed signal. It uses a tracking differentiator to accurately calculate wheel acceleration and compares it with the compensated inertial measurement unit (IMU) acceleration to accurately identify single-wheel and all-wheel slip states. Based on this, and considering the effectiveness of Global Navigation Satellite System (GNSS) signals, it adaptively selects the most suitable fusion estimation mode, such as pure wheel speed, wheel speed and IMU, or GNSS and IMU. After targeted compensation for each information source, the data is fused using algorithms such as Kalman filtering, ultimately outputting a high-precision, highly robust estimate of the vehicle's longitudinal speed. This method is particularly suitable for engineering machinery operation scenarios with uneven road surface adhesion and frequent changes in operating conditions. The method specifically includes the following processes.

[0024] Step 1: Acquire the wheel speed signal of each wheel of the vehicle, the vehicle body attitude signal, the raw GNSS signal of the Global Navigation Satellite System, and the demand torque signal that characterizes the vehicle's operating state. The vehicle body attitude signal is obtained by measuring the inertial measurement unit (IMU).

[0025] As the data foundation for all subsequent processing, the acquisition of the above-mentioned signal types specifically includes: (1) Wheel speed signal, obtained through the wheel speed sensor of each wheel, represents the rotational angular velocity of each wheel, which can be converted into linear velocity.

[0026] (2) Vehicle attitude signal, which is measured by the onboard inertial measurement unit (IMU). This signal is a data set, mainly including the vehicle's three-axis acceleration, longitudinal acceleration, pitch angle and yaw rate, etc., which are used to characterize the vehicle's motion state and spatial attitude.

[0027] (3) The raw Global Navigation Satellite System (GNSS) signal is provided by the GNSS receiver and includes the raw observation data and the calculated velocity and position information. The raw signal may have an update delay.

[0028] (4) Demand torque signal, from the vehicle controller, represents the driver's driving or braking intention and energy recovery status. The sign of the demand torque signal directly reflects the vehicle's operating status: a positive value indicates driving condition, and a negative value indicates braking or energy recovery condition.

[0029] Step 2: Based on the vehicle body attitude signal, perform motion calibration on the wheel speed signal, and select the optimal wheel speed signal from the calibrated wheel speed signals of each wheel according to the required torque signal. Specifically, the motion calibration involves using the yaw rate in the vehicle body attitude signal to compensate for the wheel speed signal of each wheel, eliminating the influence of wheel speed differences during vehicle steering on the selection of the optimal wheel speed signal, thus obtaining the calibrated wheel speed signal for each wheel.

[0030] To provide the most reliable initial vehicle speed reference for subsequent processes, the raw wheel speed signals of all wheels need to be optimized, namely, motion correction for the steering characteristics of articulated vehicles and intelligent wheel speed selection based on vehicle dynamics.

[0031] First, kinematic calibration is performed. For articulated vehicles, the inner and outer wheels move differently during steering. Directly using the original wheel speeds would introduce errors. Therefore, it is necessary to compensate for the wheel-side speeds of the left and right wheels using the yaw rate obtained from the IMU signal to eliminate the influence of pure steering motion. Based on kinematic principles, the reference wheel speeds during steering of articulated vehicles are corrected and compensated. The reference wheel-side speeds are calculated as follows: In the formula, and These are the corrected wheel speeds of the i-th wheel on the left and right sides, respectively. For wheel speed, The radius of the wheel's rolling motion. The yaw rate is angular velocity. The wheelbase is the track width. From this, we can obtain the wheel edge speed of each wheel after compensating for the yaw rate, that is, the corrected wheel speed signal of each wheel.

[0032] Next, the optimal wheel speed signal is selected. Based on all corrected wheel speed signals, instead of simply taking the average or maximum value, the optimal wheel is intelligently selected according to vehicle dynamics principles and the required torque signal. The optimal wheel is the one whose speed information is closest to the actual vehicle speed under various operating conditions, ensuring that the extracted optimal wheel information meets the needs of subsequent vehicle speed estimation. Common operating conditions can be categorized according to the positive or negative sign of the motor torque output: forward drive, reverse energy recovery, and reverse drive, forward energy recovery, etc. The selection rules are as follows: When the required torque signal is positive, i.e., under driving conditions, the ground force will resist the wheels from sliding forward relative to the ground. At this time, the wheel with the lowest wheel speed has the lowest slip ratio, which is closest to the actual vehicle speed. Therefore, the wheel with the lowest wheel speed signal value is selected as the optimal wheel. The driving conditions include forward driving and reverse driving.

[0033] When the value of the required torque signal is negative, i.e., during braking or energy recovery, the ground force will prevent the wheels from sliding backward relative to the ground. At this time, the wheel with the highest wheel speed has the lowest slip ratio, which is closest to the actual vehicle speed. Therefore, the wheel with the highest wheel speed signal value is selected as the optimal wheel. The energy recovery conditions include forward energy recovery and reverse energy recovery.

[0034] The wheel speed signal corresponding to the selected optimal wheel is the optimal wheel speed signal. This process ensures that, under any driving or braking condition, the wheel signal that is least likely to slip and best represents the true speed of the vehicle body can be used as the core reference. This improves the reliability of the basic data and thus improves the estimation accuracy.

[0035] Step 3: Based on the optimal wheel speed signal, the optimal wheel acceleration is calculated using a tracking differentiator. This optimal wheel acceleration is then compared with the vehicle's longitudinal acceleration. Based on the comparison result and a preset threshold condition, the vehicle's single-wheel slippage state and all-wheel slippage state are determined. The vehicle's longitudinal acceleration is calculated based on the vehicle attitude signal. Specifically, the calculation of the vehicle's longitudinal acceleration based on the vehicle attitude signal involves: based on the pitch angle in the vehicle attitude signal and considering the IMU's installation angle error, gravity component compensation and coordinate projection are performed on the original vehicle longitudinal acceleration in the vehicle attitude signal to obtain the calculated vehicle longitudinal acceleration.

[0036] This step is crucial for achieving adaptive switching of the fusion mode under different environments. By processing the optimal wheel speed signal using a tracking differentiator, a smooth and accurate wheel acceleration is obtained. This is then compared with the high-precision compensated IMU longitudinal acceleration of the vehicle body to determine the vehicle's slip trend. First, the optimal wheel acceleration is calculated, but directly differentiating the wheel speed signal amplifies noise. This invention uses a discrete maximum speed differential tracker for processing. The wheel acceleration is calculated using the discrete maximum speed differential tracker, and the sampling time is adjusted. and velocity factor This allows the tracking speed to match the input speed, thereby obtaining the corresponding wheel acceleration. The tracker uses the optimal wheel speed signal... For input, its internal state Another internal state used to track input velocity. The differential signal is then output. By designing the piecewise control law in the following formula and processing it through a saturation function, the acceleration state is finally updated and obtained. This value is the optimal wheel acceleration. The principle is as follows: In the formula, The input signal, i.e., the value of the optimal wheel speed signal at the kth sampling time, is the object of the entire algorithm. The goal is to track this signal and extract its derivative (acceleration). To track the state variable, representing the tracker's response to the input signal at time k. The tracked value, i.e., the smoothed wheel speed; The differential state variable represents the input signal given by the tracker at time k. The differential value, i.e., the optimal wheel acceleration, It is the updated value at the next moment; These are the design constant coefficients related to system characteristics; Sampling time; The speed factor determines the tracking speed; This is a sign function used to determine the direction of the control force in the nonlinear segment; This is a saturation function used to limit intermediate control quantities. The range is adjusted to ensure that the system's control output is not too large, thereby enhancing stability and anti-interference capabilities; To track errors, For use as an intermediate variable in nonlinear control, This is an intermediate control quantity that is not subject to amplitude limiting.

[0037] Then, the longitudinal acceleration of the vehicle body is obtained. The longitudinal acceleration is directly measured by the IMU. Including a gravitational component and affected by the installation angle, the acceleration due to vehicle motion needs to be compensated for. First, the gravitational acceleration component in the longitudinal direction of the IMU is compensated. Second, considering the installation angle error between the IMU and the vehicle, the longitudinal acceleration of the IMU is projected onto the longitudinal direction of the vehicle body, finally obtaining the true longitudinal acceleration of the vehicle body. The expression is: In the formula, This represents the calculated actual longitudinal acceleration of the vehicle body. The pitch angle measured by the IMU; It is the acceleration due to gravity; For IMU pitch angle installation error; The longitudinal acceleration measured by the IMU; Used to compensate for the gravitational component on the ramp. This is used to project acceleration from the IMU coordinate system to the vehicle body coordinate system. This compensation process solves the problem of acceleration measurement distortion caused by large attitude changes during construction machinery operation.

[0038] Next, the slip state is determined, and the optimal wheel acceleration is compared with the longitudinal acceleration of the vehicle body. The difference To determine whether a vehicle is slipping on one wheel or all wheels, the specific steps are as follows: (1) The determination of the single-wheel slip state includes: if the difference between the optimal wheel acceleration and the longitudinal acceleration of the vehicle body is greater than or equal to a first threshold, then a single-wheel slip is determined to have occurred; if the difference between the optimal wheel acceleration and the longitudinal acceleration of the vehicle body is less than the first threshold, and the duration of the difference exceeds a second threshold after satisfying a preset condition, then the single-wheel slip state is determined to have ended; wherein the preset condition includes the value of the demand torque signal being less than a third threshold or the value of the optimal wheel speed signal being greater than a fourth threshold. Specifically, if If the value is greater than or equal to the first threshold, it is determined that the optimal wheel has slipped, and the single wheel slippage flag is set. =1. To prevent misjudgment of disengagement due to continuous slight slippage of the vehicle on low-traction slopes, a strict disengagement condition is set. This condition is added to accommodate the following operating conditions: when on a slope with poor traction, the vehicle has difficulty climbing the slope, and all four wheels may be engaged in anti-slip driving, slowly slipping at low speeds, maintaining a near-stationary, rolling, or very slow start-up state at low speeds. In this situation, the rotational acceleration and longitudinal acceleration of the four wheels are very low, possibly below the threshold, but still under anti-slip control, and the vehicle speed should not be estimated from wheel speed. The disengagement condition is: only when The slippage is considered to have ended only if the required torque signal value is less than the first threshold, and simultaneously meets the condition that the required torque signal value is less than the third threshold or the optimal wheel speed signal value is greater than the fourth threshold, and continues for a period exceeding the second threshold. =0. This design enhances the robustness and accuracy of glide detection in complex, continuous operation scenarios.

[0039] The first threshold can be set based on the noise level of the vehicle's acceleration sensor and typical slip conditions, for example, a value of 1.0 m / s²; the second threshold is used for vibration reduction, for example, a value of 0.5 s; the third threshold can be set based on the minimum stable torque of the drive system, for example, a value of 5000 N·m; and the fourth threshold can be set based on the minimum stable vehicle speed on extremely low-traction surfaces, for example, a value of 0.2 m / s. These thresholds can be adjusted according to specific vehicle models and typical operating conditions.

[0040] (2) The determination of the all-wheel slippage state includes: if all wheels are determined to have single-wheel slippage, then all-wheel slippage is determined to have occurred. Specifically, if the system detects all wheels... If both values ​​are 1, then a full-wheel slippage is determined to have occurred, and the full-wheel slippage flag is set. =1.

[0041] Step 4: Based on the determined slip state and the validity of the original GNSS signal, select the corresponding target mode from a variety of predefined vehicle speed fusion estimation modes; the vehicle speed fusion estimation modes include pure wheel speed mode, wheel speed and IMU fusion mode, and GNSS and IMU fusion mode.

[0042] Based on the aforementioned slip state and GNSS signal validity, the optimal fusion strategy is adaptively selected, and the corresponding signals are corrected as necessary before data fusion. The system predefines several vehicle speed fusion estimation modes: when neither single-wheel slip nor full-wheel slip occurs, the pure wheel speed mode is selected; when single-wheel slip occurs but not full-wheel slip, the wheel speed and IMU fusion mode is selected; when full-wheel slip occurs and the original GNSS signal is valid, the GNSS and IMU fusion mode is selected; when full-wheel slip occurs and the original GNSS signal is invalid, the wheel speed and IMU fusion mode is selected, and the fusion weight of the IMU signal is increased. The specific selection logic is shown in Table 1.

[0043] Table 1 Selection Logic for Multi-Mode Vehicle Speed ​​Fusion Estimation In addition, specific signals need to be corrected before fusion. GNSS signal delay correction is triggered only in the GNSS+IMU fusion mode. Specifically, delay correction of the raw GNSS signal involves compensating for the time delay of the raw GNSS signal based on the vehicle's longitudinal acceleration to obtain a delay-corrected GNSS velocity signal. Since GNSS signals have an inherent update delay of approximately 50-250ms, direct use would lead to estimation lag. The correction method in this embodiment is as follows: within several cycles before the GNSS signal update, the vehicle's longitudinal acceleration obtained from the aforementioned calculation is integrated to recursively predict the velocity; when a new raw GNSS velocity value arrives, the integrated value is used to compensate for the time delay, resulting in a delay-corrected GNSS velocity signal that is strictly aligned with the current time. This overcomes the impact of satellite signal lag on real-time estimation and provides high-quality GNSS velocity information for subsequent fusion.

[0044] Step 5: Based on the target mode, perform a fusion estimation on the optimal wheel speed signal and the vehicle longitudinal acceleration to obtain the estimated vehicle longitudinal speed at the current moment; wherein when the target mode is the GNSS and IMU fusion mode, the fusion estimation also includes the GNSS speed signal obtained after delay correction of the original GNSS signal.

[0045] Regardless of the mode, the core fusion algorithm preferentially employs Kalman filtering to achieve optimal fusion of multi-source information. Kalman filtering operates recursively through two steps: prediction and update. Its state-space model can be represented as: ; ; in, Let k be the state vector of the system at time k, which usually includes vehicle speed and acceleration. For the observation vector, Here is the state transition matrix. For the input control matrix, For the observation matrix, and These represent process noise and observation noise, respectively. State vector Selected as The observation matrix Determined based on the selected fusion mode. For example, in the wheel speed + IMU fusion mode, the observation vector... for , Indicates transpose, then the observation matrix Designed for Process noise covariance matrix and observation noise covariance matrix It is a key parameter in filter design. It reflects the uncertainty of system state prediction and can be set according to the acceleration variation range of the vehicle dynamics model and typical working conditions; This reflects the noise level of each sensor's observations and can be determined based on the measured accuracy or datasheet specifications of devices such as wheel speed sensors and IMUs. In modes requiring increased IMU signal fusion weights, this can be achieved by reducing the matrix... This is achieved by using the diagonal element values ​​corresponding to the observed longitudinal acceleration of the vehicle body.

[0046] The current state and covariance are predicted based on the state estimate and system model from the previous time step. The difference between the current actual observations (which may be based on the optimal wheel speed signal, vehicle longitudinal acceleration, or GNSS speed information depending on the selected mode) and the predicted observations is then used in conjunction with the calculated Kalman gain. The predicted state is optimally corrected to obtain the optimal state estimate for the current moment. The vehicle speed component in the final state estimate is the output estimate of the vehicle's longitudinal speed at the current moment. In the wheel speed + IMU mode with enhanced IMU weights, the observation noise covariance corresponding to the IMU acceleration in the filter is adjusted to reduce... The corresponding values ​​in the matrix can be used to increase the fusion weight.

[0047] This embodiment provides a multi-environment engineering vehicle speed estimation system. When executed, the system implements the aforementioned multi-environment engineering vehicle speed estimation method, including: The signal acquisition module is configured to acquire wheel speed signals of each wheel of the vehicle, vehicle attitude signals, raw GNSS signals from the Global Navigation Satellite System, and demand torque signals characterizing the vehicle's operating state, wherein the vehicle attitude signals are obtained by measuring the inertial measurement unit (IMU). The wheel speed processing module is configured to perform motion correction on the wheel speed signal based on the vehicle body posture signal, and select the optimal wheel speed signal from the corrected wheel speed signals of each wheel according to the required torque signal; The slippage detection module is configured to calculate the optimal wheel acceleration based on the optimal wheel speed signal using a tracking differentiator, compare the optimal wheel acceleration with the vehicle body longitudinal acceleration, and determine the single-wheel slippage state and all-wheel slippage state of the vehicle based on the comparison result, wherein the vehicle body longitudinal acceleration is calculated based on the vehicle body attitude signal; The mode selection module is configured to select a corresponding target mode from a variety of predefined vehicle speed fusion estimation modes based on the determined slip state and the validity of the raw GNSS signal; the vehicle speed fusion estimation modes include pure wheel speed mode, wheel speed and IMU fusion mode, and GNSS and IMU fusion mode; The speed fusion module is configured to perform a fusion estimation of the optimal wheel speed signal and the vehicle longitudinal acceleration based on the target mode to obtain the vehicle longitudinal speed estimate at the current moment; wherein when the target mode is the GNSS and IMU fusion mode, the speed fusion module is further configured to perform delay correction on the original GNSS signal to obtain a GNSS speed signal, and incorporate the GNSS speed signal into the fusion estimation.

[0048] In summary, this invention, through optimal wheel speed selection, precise slip detection, and adaptive multi-mode fusion, ensures that the system can always call upon the most reliable information source for fusion under adverse conditions such as tire slippage and sudden changes in adhesion, overcoming the poor adaptability of single methods. Before fusion, the invention performs steering compensation on wheel speed, gravity and installation error compensation on IMU acceleration, and delay compensation on GNSS speed. This multi-level, forward-looking signal preprocessing improves the accuracy of each information source from the outset, laying a solid foundation for high-precision fusion. Even in extreme conditions such as full-wheel slippage and GNSS failure, the system can still provide effective vehicle speed estimation through a backoff mode, ensuring functional continuity. Furthermore, all parts of the algorithm are optimized for embedded platforms, meeting the real-time requirements of engineering machinery control while ensuring accuracy.

[0049] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] Contents not described in detail in this specification are prior art known to those skilled in the art. It is hereby indicated that the above description is intended to help those skilled in the art understand this invention, but does not limit the scope of protection of this invention. Any equivalent substitutions, modifications, improvements, or simplifications of the above descriptions that do not depart from the essential content of this invention fall within the scope of protection of this invention.

Claims

1. A method for estimating the speed of engineering vehicles in multiple environments, characterized in that, Includes the following steps: The vehicle acquires wheel speed signals for each wheel, vehicle attitude signals, raw GNSS signals from the Global Navigation Satellite System, and demand torque signals characterizing the vehicle's operating state. The vehicle attitude signals are obtained by measuring the vehicle's attitude using an inertial measurement unit (IMU). The wheel speed signal is kinematically calibrated based on the vehicle body posture signal, and the optimal wheel speed signal is selected from the calibrated wheel speed signals of each wheel according to the required torque signal. Based on the optimal wheel speed signal, the optimal wheel acceleration is calculated by a tracking differentiator, and the optimal wheel acceleration is compared with the vehicle longitudinal acceleration. Based on the comparison result and combined with a preset threshold condition, the single-wheel slip state and the all-wheel slip state of the vehicle are determined, wherein the vehicle longitudinal acceleration is calculated based on the vehicle attitude signal. Based on the determined slip state and the validity of the raw GNSS signal, a corresponding target mode is selected from a variety of predefined vehicle speed fusion estimation modes; the vehicle speed fusion estimation modes include pure wheel speed mode, wheel speed and IMU fusion mode, and GNSS and IMU fusion mode; Based on the target mode, the optimal wheel speed signal and the vehicle longitudinal acceleration are fused and estimated to obtain the vehicle longitudinal speed estimate at the current moment; wherein when the target mode is the GNSS and IMU fusion mode, the fusion estimation also includes the GNSS speed signal obtained after delay correction of the original GNSS signal.

2. The method for estimating the speed of a multi-environment engineering vehicle according to claim 1, characterized in that, The motion correction specifically involves using the yaw rate in the vehicle body posture signal to compensate the wheel speed signal of each wheel, eliminating the influence of wheel speed differences during vehicle steering on the selection of the optimal wheel speed signal, and obtaining the corrected wheel speed signal of each wheel.

3. The method for estimating the speed of a multi-environment engineering vehicle according to claim 1, characterized in that, The selection of the optimal wheel speed signal specifically involves: When the value of the required torque signal is positive, the wheel with the smallest wheel speed signal value is selected from the corrected wheel speed signals of each wheel as the optimal wheel, and its wheel speed signal is the optimal wheel speed signal. When the value of the required torque signal is negative, the wheel with the largest wheel speed signal value is selected from the corrected wheel speed signals of each wheel as the optimal wheel, and its wheel speed signal is the optimal wheel speed signal.

4. The method for estimating the speed of a multi-environment engineering vehicle according to claim 1, characterized in that, The determination of the single-wheel slippage state includes: If the difference between the optimal wheel acceleration and the longitudinal acceleration of the vehicle body is greater than or equal to the first threshold, then it is determined that a single wheel slip has occurred. If the difference between the optimal wheel acceleration and the longitudinal acceleration of the vehicle body is less than the first threshold, and the duration of the difference exceeds the second threshold after the preset conditions are met, then the single-wheel slippage state is determined to end; wherein the preset conditions include the value of the demand torque signal being less than the third threshold or the value of the optimal wheel speed signal being greater than the fourth threshold.

5. The method for estimating the speed of a multi-environment engineering vehicle according to claim 4, characterized in that, The determination of the full-wheel slippage state includes: if all wheels are determined to have single-wheel slippage, then full-wheel slippage is determined to have occurred.

6. The method for estimating the speed of a multi-environment engineering vehicle according to claim 1, characterized in that, The selection logic for the various predefined vehicle speed fusion estimation modes includes: When neither single-wheel slippage nor full-wheel slippage occurs, select the pure wheel speed mode; When a single wheel slip occurs but the entire wheel does not slip, select the wheel speed and IMU fusion mode; When full wheel slip occurs and the original GNSS signal is valid, the GNSS and IMU fusion mode is selected; When full wheel slip occurs and the original GNSS signal fails, select the wheel speed and IMU fusion mode and increase the fusion weight of the IMU signal.

7. The method for estimating the speed of a multi-environment engineering vehicle according to claim 1, characterized in that, The fusion estimation is implemented using the Kalman filter algorithm.

8. The method for estimating the speed of a multi-environment engineering vehicle according to claim 1, characterized in that, The longitudinal acceleration of the vehicle body is calculated based on the vehicle body attitude signal. Specifically, based on the pitch angle in the vehicle body attitude signal and combined with the installation angle error of the IMU, the original longitudinal acceleration of the vehicle body in the vehicle body attitude signal is compensated for by gravity component and coordinate projection to obtain the calculated longitudinal acceleration of the vehicle body.

9. The method for estimating the speed of a multi-environment engineering vehicle according to claim 1, characterized in that, The original GNSS signal is delayed and corrected by performing time delay compensation on the original GNSS signal based on the longitudinal acceleration of the vehicle body to obtain the delayed and corrected GNSS velocity signal.

10. A multi-environment engineering vehicle speed estimation system, characterized in that, When the system is executed, it implements the multi-environment engineering vehicle speed estimation method as described in claims 1-9, including: The signal acquisition module is configured to acquire wheel speed signals of each wheel of the vehicle, vehicle attitude signals, raw GNSS signals from the Global Navigation Satellite System, and demand torque signals characterizing the vehicle's operating state, wherein the vehicle attitude signals are obtained by measuring the inertial measurement unit (IMU). The wheel speed processing module is configured to perform motion correction on the wheel speed signal based on the vehicle body posture signal, and select the optimal wheel speed signal from the corrected wheel speed signals of each wheel according to the required torque signal; The slippage detection module is configured to calculate the optimal wheel acceleration based on the optimal wheel speed signal using a tracking differentiator, compare the optimal wheel acceleration with the vehicle body longitudinal acceleration, and determine the single-wheel slippage state and all-wheel slippage state of the vehicle based on the comparison result, wherein the vehicle body longitudinal acceleration is calculated based on the vehicle body attitude signal; The mode selection module is configured to select a corresponding target mode from a variety of predefined vehicle speed fusion estimation modes based on the determined slip state and the validity of the raw GNSS signal; the vehicle speed fusion estimation modes include pure wheel speed mode, wheel speed and IMU fusion mode, and GNSS and IMU fusion mode; The speed fusion module is configured to perform a fusion estimation of the optimal wheel speed signal and the vehicle longitudinal acceleration based on the target mode to obtain the vehicle longitudinal speed estimate at the current moment; wherein when the target mode is the GNSS and IMU fusion mode, the speed fusion module is further configured to perform delay correction on the original GNSS signal to obtain a GNSS speed signal, and incorporate the GNSS speed signal into the fusion estimation.

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