Ramp information and vehicle running state rapid acquisition method

Abstract

The invention relates to a method for quickly acquiring ramp information and a vehicle running state, which is based on a first acceleration sensor parallel to the vehicle running direction and a second acceleration sensor perpendicular to the first acceleration sensor. Synchronously measuring to obtain a first acceleration component along the slope surface direction and a second acceleration component vertical to the slope surface direction; according to the projection relation between the second acceleration component and the gravitational acceleration in the direction perpendicular to the slope surface, the slope angle and the running acceleration of the current ramp are analyzed; and torque compensation control is conducted on a vehicle driving motor through the slope angle and the running acceleration. Aiming at the limitation of ramp detection of the existing GPS and gyroscope, a ramp information and vehicle running state acquisition system which is low in cost, high in real-time performance and adaptive to a whole scene is constructed by taking double vertical acceleration sensors as a basic core; the problems of slow ramp detection response, high cost, insufficient precision and load interference of the industrial vehicle in a closed and complex scene are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, specifically to a method for rapidly acquiring slope information and vehicle operating status. Background Technology

[0002] In scenarios such as logistics warehousing, port freight, and manufacturing workshops, electric counterbalance forklifts need to frequently travel on ramps, such as loading and unloading platform ramps and ramps between warehouse floors. Their ramp travel status directly determines driving safety and energy efficiency. Misjudgment of the ramp angle may cause the vehicle to slip on the ramp, insufficient drive torque may cause the engine to stall, and loss of control of driving acceleration may increase the risk of cargo tipping and vehicle rollover. At the same time, accurate status data can guide the optimization of drive motor torque and avoid energy waste caused by excessive or insufficient power.

[0003] With the trend of electrification and intelligentization of industrial vehicles, the market demand for real-time, accurate, and low-cost ramp condition detection is becoming increasingly urgent. Traditional detection solutions mostly rely on GPS positioning, gyroscope inertial measurement, or indirect calculation by a single sensor. However, due to the characteristics of industrial scenarios (such as enclosed environments like indoor warehouses and underground parking garages) and cost control requirements, existing technologies cannot achieve a balance between accuracy, response speed, and cost, and targeted technological breakthroughs are urgently needed.

[0004] GPS signals rely on direct satellite line-of-sight communication. In the core operating scenarios of electric counterbalance forklifts (such as indoor warehouses, underground garages, inside containers, and workshops), the signal can be blocked by walls, metal structures, and goods, leading to positioning failure or a sharp increase in elevation measurement errors, which in turn causes complete distortion of slope angle calculations. Even in outdoor scenarios, heavy rain, fog, haze, and tall buildings can cause GPS signal attenuation, reducing the accuracy of slope detection and failing to meet safety control requirements.

[0005] Meanwhile, GPS signal sampling periods are typically 1-5Hz, and 2-3 consecutive sampling cycles are required to calculate the effective elevation difference and horizontal distance, resulting in a long delay in the actual slope angle output. When an electric counterbalance forklift is traveling on a slope, the torque compensation of the drive motor needs to respond within a very short time (otherwise, power interruption or slippage may occur). This delay directly leads to torque control lag, increasing the risk of driving on slopes. Therefore, existing technologies require high-precision GPS modules, signal amplification circuits, and complex complementary filter chips, resulting in high overall vehicle hardware costs.

[0006] Therefore, existing technologies for detecting slope angles largely rely on sensors such as GPS and gyroscopes, which suffer from slow response, high cost, or insufficient accuracy. Thus, it is necessary to research a method for rapidly acquiring slope information and vehicle operating status. Summary of the Invention

[0007] Therefore, the purpose of this invention is to provide a method for rapidly acquiring slope information and vehicle operating status, which effectively solves the problems of slow response and high cost of existing slope detection methods.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is: a method for rapidly acquiring slope information and vehicle operating status.

[0009] Based on a first acceleration sensor set parallel to the vehicle's direction of travel and a second acceleration sensor set perpendicular to the first acceleration sensor, the first acceleration component along the slope direction and the second acceleration component perpendicular to the slope direction are measured synchronously.

[0010] Based on the projection relationship between the second acceleration component and the gravitational acceleration in the direction perpendicular to the slope, the current slope angle is analyzed.

[0011] Based on the relationship between the first acceleration component and the projection of gravitational acceleration along the slope, the vehicle's driving acceleration is analyzed.

[0012] The obtained slope angle and driving acceleration are used to perform torque compensation control on the vehicle drive motor.

[0013] Furthermore, the actual acceleration of the vehicle along the slope direction is obtained, and the ratio of the difference between the actual acceleration and the driving acceleration to the actual acceleration is used as the accuracy. The accuracy is then compared with a threshold to obtain the reliability.

[0014] Furthermore, if the accuracy is lower than the threshold, the analysis result is determined to be reliable and used for subsequent torque compensation control; if the accuracy is higher than or equal to the threshold, the analysis result is determined to be unreliable and a backup control strategy is activated.

[0015] Furthermore, the activation of the backup control strategy includes: discarding the current analysis result and using the most recently determined reliable slope angle and driving acceleration data for torque compensation control, or using slope information estimated based on the vehicle driving resistance balance equation for torque compensation control.

[0016] Furthermore, the additional torque required to overcome the component of gravity in the slope direction is calculated based on the slope angle, and the additional torque is superimposed with the compensation torque determined based on the driver's required torque and the actual driving acceleration, as the total target torque output of the drive motor.

[0017] Furthermore, based on pressure sensors deployed at the vehicle's load-bearing locations, vehicle load data is collected in real time. A load slope angle correction factor is constructed based on the vehicle type, and the slope angle is corrected using this correction factor. The calculation process of the correction factor includes the following steps:

[0018] S1. Obtain basic vehicle parameters, including the vehicle's rated load capacity and maximum climbing angle;

[0019] S2. Calculate the load ratio and slope ratio; the load ratio is the ratio of real-time load data to rated load mass; the slope ratio is the ratio of the analyzed slope angle to the maximum climbing angle.

[0020] S3. Combine the load ratio and slope ratio to form the first influencing factor, and use the load ratio as the second influencing factor. Weight the first influencing factor and the second influencing factor to form the correction factor.

[0021] S4. Verify the validity of the obtained correction factor. If the correction factor exceeds the preset range, call the average correction factor of the same working conditions in history as the temporary valid correction factor output and trigger an alarm reminder; if the correction factor is within the preset range, output the valid correction factor.

[0022] Furthermore, the weights of the first and second impact factors are adjusted based on the range of credibility.

[0023] Furthermore, if the credibility is determined to be credible, the weights of the first and second impact factors are reduced; if the credibility is determined to be unreliable, the weights of the first and second impact factors are increased.

[0024] Furthermore, when the credibility is determined to be unreliable, based on the correction of the initial slope angle by the correction factor, the estimated slope value based on the vehicle longitudinal dynamics equation is introduced, and the estimated slope value and the corrected slope angle are weighted and fused to obtain the final slope angle.

[0025] Furthermore, the raw signals output by the first and second accelerometers are filtered for noise reduction and outlier removal; the filtering for noise reduction is implemented using a discrete Kalman filter.

[0026] The beneficial effects of the above technical solution are as follows: In view of the limitations of existing GPS and gyroscope in slope detection, this invention uses dual vertical acceleration sensors and multi-dimensional verification and optimization as the core to build a low-cost, high-real-time, and all-scenario adaptable slope information and vehicle operation status acquisition system, which effectively solves the problems of slow response, high cost, insufficient accuracy and load interference in slope detection of industrial vehicles in closed and complex scenarios.

[0027] In implementation, two accelerometers, one parallel and one perpendicular to the driving direction, replace high-cost, environmentally dependent equipment to directly capture the mechanical components of the slope. This adapts to core operational scenarios such as indoor warehouses and underground parking garages. Replacing high-cost equipment with low-cost sensors reduces hardware costs and addresses signal obstruction issues common with traditional equipment in enclosed environments like indoors and underground. The accelerometers have a sampling period significantly longer than GPS, resulting in shorter parameter resolution delays and meeting the rapid response requirements for torque compensation. Through load correction and dynamic fusion, the slope angle resolution accuracy is high, with significant improvements in accuracy even in unreliable scenarios.

[0028] Meanwhile, a judgment system is constructed based on the actual acceleration obtained from the differential wheel speed signal. This system, combined with the reuse of historical reliable data and a backup strategy for estimating the vehicle's longitudinal dynamics equations, ensures the reliability of the analytical data. Multi-level verification and backup strategies mitigate the risk of single data failures. Load correction eliminates interference from vehicle attitude deviation, and dynamic torque compensation prevents safety hazards such as uphill slippage and downhill lurching, thereby reducing the accident rate on slopes.

[0029] Meanwhile, this invention collects real-time load data through pressure sensors at the load location, constructs a load slope angle correction factor adapted to the vehicle type, dynamically eliminates vehicle body attitude deviation and sensor reference drift caused by load; and further correlates the credibility judgment results to adjust the weight of the correction factor, and further integrates the dynamic estimation slope value in untrusted scenarios to achieve dynamic matching of accuracy requirements and correction strength, ensuring the slope angle analysis accuracy under all working conditions.

[0030] In summary, this invention constructs a complete system for rapidly acquiring ramp information and vehicle operating status through lightweight sensors. The core of this invention lies in: directly analyzing parameters using a mechanical model to avoid environmental interference with signals; ensuring data reliability through multi-dimensional verification and backup strategies to address the risk of single sensor failure; and adapting to load and reliability changes through a dynamic correction mechanism to ensure accuracy across all scenarios. This invention meets the safe ramp operation requirements of industrial vehicles such as electric counterbalance forklifts in various scenarios, providing a low-cost, highly reliable status detection solution for the electrification and intelligentization of industrial vehicles, and possesses significant engineering application value and market promotion potential. Attached Figure Description

[0031] Figure 1 This is a system flowchart of the present invention;

[0032] Figure 2 This is a schematic diagram illustrating the decomposition principle of gravitational acceleration in this invention.

[0033] Figure 3 This is a flowchart illustrating the decision logic of the present invention;

[0034] Figure 4 This is a block diagram of the data acquisition system of the present invention;

[0035] Figure 5 This is a flowchart of the data preprocessing process of the present invention;

[0036] Figure 6 This is a flowchart illustrating the analysis process of slope angle and driving acceleration in this invention.

[0037] Figure 7 Flowchart of the credibility verification process of this invention;

[0038] Figure 8 Here is a flowchart of the impact factor generation process;

[0039] Figure 9 This is a flowchart of the process for revising the impact factor based on credibility. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0041] Example 1

[0042] This embodiment aims to provide a method for rapidly acquiring slope information and vehicle operating status. It is primarily used to detect the slope angle during vehicle operation, especially on slopes, and to control the vehicle's operating status based on the slope angle. Existing technologies often rely on GPS, gyroscopes, and other devices for slope detection, which has limitations in detecting and controlling the slope information and operating status of industrial vehicles such as electric counterbalance forklifts. However, these technologies have high equipment requirements and are dependent on specific working environments, resulting in slow response, high cost, or insufficient accuracy. Therefore, this embodiment provides a method for rapidly acquiring slope information and vehicle operating status. Its core concept is to overcome the limitations of traditional slope detection methods that rely on GPS and gyroscopes. Using dual vertical acceleration sensors as the core principle and leveraging mechanical model analysis, it achieves low-cost, high-real-time, and accurate acquisition of slope information and operating status for industrial vehicles such as electric counterbalance forklifts in all scenarios.

[0043] When implementing Figure 1-2 As shown in the figure, this embodiment provides a method for quickly obtaining slope information and vehicle operating status. Specifically, it uses two vertically deployed acceleration sensors to directly capture key mechanical components when driving on a slope, calculates core parameters by combining the projection law of gravitational acceleration, and ensures data reliability through multi-dimensional verification and backup strategies. Finally, it outputs accurate parameters to guide torque control, thus solving the defects of traditional technologies such as environmental occlusion, response delay, high cost and load interference in principle.

[0044] When implementing Figure 2-3As shown, a first acceleration sensor, which is set parallel to the vehicle's driving direction, and a second acceleration sensor, which is perpendicular to the first acceleration sensor, synchronously measure the first acceleration component along the slope direction and the second acceleration component perpendicular to the slope direction. The first acceleration sensor is installed parallel to the vehicle's driving direction (longitudinal), and its detection axis is consistent with the vehicle's forward and backward directions, and is used to capture acceleration changes along the slope.

[0045] like Figure 4 As shown, the second accelerometer is mounted vertically to the first accelerometer, with its detection axis perpendicular to the vehicle's direction of travel. It is used to capture acceleration changes on a vertical slope. Both sensors are rigidly connected to the vehicle chassis. The two accelerometers output raw signals in real time, which are directly transmitted to the data processing unit. The data processing unit simultaneously receives vehicle wheel speed signals (for calculating actual acceleration) and drive motor torque signals (for calculating resistance balance in backup strategies). After completing signal processing, parameter parsing, and reliability assessment using a preset algorithm, it transmits the final slope angle, driving acceleration, and torque control commands to the vehicle drive control system, forming a control logic of acquisition, processing, and control.

[0046] like Figure 5 As shown, in order to eliminate the impact of sensor noise, road bumps and other interference on measurement accuracy, the original signal needs to be preprocessed. In this embodiment, the original signals output by the first acceleration sensor and the second acceleration sensor are filtered and denoised and outlier is removed; the filtering and denoising is implemented using a discrete Kalman filter.

[0047] For the continuous dynamic signal output by the accelerometer, a discrete Kalman filter algorithm is used to establish a signal model. The original sensor signal is treated as the real signal and measurement noise. By using preset process excitation noise covariance (reflecting the stability of the system's dynamic characteristics) and measurement noise covariance (reflecting the noise level of the sensor itself), the optimal estimate for each sampling is iteratively calculated. The core logic is to first predict the prior estimate of the current signal using historical data, then correct the estimate by combining it with the current actual measurement value, and finally filter out high-frequency noise (such as instantaneous signal fluctuations caused by road bumps) and retain the low-frequency effective signal that reflects the true motion state of the vehicle.

[0048] Considering that the sensor may output extreme values ​​due to instantaneous impacts (such as going over speed bumps or collisions with swaying goods), this embodiment uses a sliding window and statistical threshold method to eliminate outliers. By setting a sliding window of fixed duration, the mean and standard deviation of the signal within the window are calculated in real time. If the signal value of a certain sampling point exceeds the reasonable range of the mean and standard deviation, it is determined to be an outlier and replaced with the signal value of the previous valid sampling point within the window, thus avoiding significant deviations in subsequent parameter analysis caused by outliers.

[0049] like Figure 6 As shown in the illustration, in calculating the slope angle, this embodiment first analyzes the current slope angle based on the relationship between the second acceleration component and the projection of gravitational acceleration in the direction perpendicular to the slope. In the scenario of driving on a slope, the Earth's gravitational acceleration can be decomposed into a component perpendicular to the slope and a component along the slope. The vertical slope acceleration component measured by the second acceleration sensor is essentially the superposition of the projection of gravitational acceleration perpendicular to the slope and the vehicle's motion acceleration in the direction perpendicular to the slope (since the motion acceleration in the direction perpendicular to the slope is extremely small when the vehicle is driving along the slope, it can be ignored). Therefore, the second acceleration component can be considered approximately equal to the projection of gravitational acceleration in the direction perpendicular to the slope. The current slope angle can be analyzed by using inverse trigonometric function calculations.

[0050] The acceleration due to gravity (g = 9.81 m / s²) is a constant vector. In the slope scenario, it can be decomposed into two components along two directions: parallel to the slope and perpendicular to the slope. The magnitude of the component perpendicular to the slope is... The force is perpendicular to the slope and points towards the ground. This component of the force only affects the pressure of the vehicle on the road surface (such as causing tire compression) and has no direct effect on the acceleration along the slope. Therefore, it can be ignored in the analysis of driving acceleration.

[0051] The specific calculation formula is as follows:

[0052]

[0053]

[0054] In the formula The second acceleration component, The slope angle is denoted by .

[0055] Based on the relationship between the first acceleration component and the projection of gravitational acceleration along the slope, the vehicle's driving acceleration is analyzed. The acceleration component along the slope measured by the first acceleration sensor is the superposition of the vehicle's own driving acceleration and the projection of gravitational acceleration along the slope. Combined with the already analyzed slope angle, the vehicle's true driving acceleration can be separated through algebraic operations, eliminating the interference of the gravitational component on the judgment of driving status.

[0056] The magnitude of the component force parallel to the slope is The direction of this force is along the slope towards the bottom of the slope (whether going uphill or downhill, the direction of this component force is always to resist the vehicle's movement towards the top of the slope and to push the vehicle towards the bottom of the slope). This component force will be directly superimposed on the vehicle's acceleration along the slope and become an important part of the measurement signal of the first acceleration sensor.

[0057] In practice, when going uphill, the component of gravity along the slope is opposite to the direction of travel. The formula for calculating the first acceleration component is as follows:

[0058]

[0059] In the formula, The first acceleration component, This refers to acceleration during travel.

[0060] When going downhill, the component of gravity along the slope is in the same direction as the vehicle's movement. The specific relationship between the second acceleration component and the vehicle's descent is as follows:

[0061]

[0062] This embodiment uses the driving acceleration 'a' obtained from the above formula, and utilizes the obtained slope angle and driving acceleration to perform torque compensation control on the vehicle drive motor. Specifically, the compensation strategy involves calculating the additional torque required to overcome the component of gravity in the slope direction based on the slope angle, and then superimposing this additional torque with the compensation torque determined based on the driver's required torque and the actual driving acceleration, as the total target torque output of the drive motor.

[0063] In practical implementation, the core function of the additional torque is to counteract the effect of the component of gravity along the slope on vehicle movement. Its calculation is based on the mechanical logic of gravity component and torque conversion, ensuring that the vehicle obtains equivalent power on a slope as it does on a flat road. The specific calculation formula is as follows:

[0064]

[0065] In the formula The slope angle obtained from the analysis. For the total mass of the vehicle. The tire's rolling radius, For transmission efficiency, The total transmission ratio of the transmission system. This represents the magnitude of the component of gravity along the slope.

[0066] The driver's required torque is quantified through the accelerator pedal opening, directly reflecting the driver's power demand. The quantification of required torque is achieved based on the accelerator pedal opening and torque mapping rules. Specifically, the following data serves as an example for illustration:

[0067] Pedal opening 0%-30%: =0.2−0.5 (low-speed, stable driving)

[0068] Pedal opening 30%-70%: =0.5−1 (low-speed, stable driving)

[0069] Pedal opening 70%-100%: =1−1.5 (Low-speed, stable driving)

[0070] The deviation is calculated based on the target acceleration, and the compensation torque is obtained. The specific calculation formula is as follows:

[0071]

[0072]

[0073] In the formula, The actual acceleration obtained from the wheel speed difference. This is the proportionality coefficient. The rated torque of the motor. To compensate for torque.

[0074] The total target torque is calculated using an algebraic superposition rule, and the specific formula is as follows:

[0075]

[0076] In the formula, (Driver-demanded torque) is the fundamental torque component reflecting the driver's operational intention, directly determining the reference direction and basic strength of the vehicle's power output. By pressing or releasing the accelerator pedal, the driver translates their subjective intention to accelerate, maintain a constant speed, or decelerate the vehicle into a quantifiable torque command, which provides additional torque. Slope compensation, dynamic compensation torque The smoothness adjustment provides a benchmark framework to ensure that the final total target torque does not contradict the driver's core operating intentions.

[0077] Specifically, this embodiment abandons high-cost and environmentally dependent GPS and gyroscopes, and adopts two vertically deployed acceleration sensors (longitudinal along the driving direction and vertical perpendicular to the driving direction) to directly capture key mechanical components of slope driving, reducing hardware costs and installation complexity, while adapting to enclosed scenarios such as indoor warehouses and underground parking garages. In terms of data processing, the raw signal is preprocessed by using discrete Kalman filtering for noise reduction and sliding window outlier removal, and the credibility is verified by comparing the actual acceleration with wheel speed difference. It is also equipped with historical reliable data reuse and dynamic equation estimation. In terms of torque control, this embodiment constructs a torque superposition control strategy based on analytical and precise parameters, which includes additional torque (to overcome gravity), driver demand torque (operational intention), and dynamic compensation torque (smoothness adjustment), to achieve a balance between power and safety when driving on slopes, and fundamentally solves the defects of traditional technologies such as environmental obstruction, slow response, and insufficient accuracy.

[0078] Example 2

[0079] Based on Example 1, this example further verifies the calculated driving speed based on the actual acceleration, using the actual acceleration as the benchmark for credibility verification, directly reflecting the true motion state of the vehicle's wheel axles, and avoiding interference from sensor model assumptions or noise.

[0080] This implementation example Figure 7 As shown, the acceleration is achieved through differential calculation of wheel speed signals. The core is to quantify the acceleration by the change in wheel speed. The wheel speed signal is taken from the ABS wheel speed sensor or CAN bus wheel speed data of the electric counterbalance forklift. First, a preprocessing operation is performed, using a sliding window mean filter to filter out wheel speed fluctuations caused by wheel tooth machining errors and road bumps. At the same time, the wheel speed change rate is calculated. When the wheel speed change rate exceeds the standard, it is judged as abnormal data and is removed to avoid slippage causing distortion of the actual acceleration calculation.

[0081] Then, the actual acceleration of the vehicle along the slope is obtained. The actual acceleration is the change in wheel speed per unit time, which needs to be synchronized with the sampling period of the first acceleration sensor to ensure consistent data timestamps. The specific calculation formula is as follows:

[0082]

[0083] In the formula, The wheel speed after filtering in the current cycle. Since the wheel speed direction is parallel to the slope when the forklift is traveling along the slope, no additional correction is needed, and the longitudinal wheel speed can be used directly; The wheel speed after filtering in the previous cycle. The sampling period is set to be consistent with that of the accelerometer to avoid deviations caused by time differences.

[0084] The accuracy is measured by the ratio of the difference between the actual acceleration and the driving acceleration to the actual acceleration. The core of this accuracy is the relative deviation, not the absolute deviation, which avoids misjudgments caused by the magnitude of the acceleration value. In this embodiment, the accuracy is calculated as the ratio of the deviation between the analyzed driving acceleration and the actual acceleration. The specific calculation formula is as follows:

[0085]

[0086] when When the value is not equal to zero, the vehicle is in motion, and the accuracy reflects the percentage deviation between the analytical acceleration and the actual acceleration. When the value equals zero, the vehicle is stationary, and the accuracy is... Record it as 0.

[0087] Reliability is obtained by comparing accuracy with a threshold, and reliability is qualitatively judged by comparing accuracy with the threshold. First, the threshold is the critical value that distinguishes between reliable and unreliable. Its specific value is calibrated through multi-condition experiments, rather than a fixed value, to ensure that it matches the forklift type and operation scenario. When determining the threshold, analytical acceleration and actual acceleration data need to be collected under different slopes and different loads, and the accuracy of each set of data is calculated. The maximum accuracy of more than 95% of the data is used as the threshold.

[0088] If the accuracy is lower than the threshold, the analysis result is deemed reliable and used for subsequent torque compensation control; if the accuracy is higher than or equal to the threshold, the analysis result is deemed unreliable and a backup control strategy is activated.

[0089] Activating backup control strategies include: discarding the current analysis results and using the most recently determined reliable slope angle and driving acceleration data for torque compensation control, or using slope information estimated based on the vehicle driving resistance balance equation for torque compensation control.

[0090] In this embodiment, the backup strategy is executed in priority order, prioritizing the use of historical reliable data (fast response, high accuracy). If no historical data is available, dynamic estimation is used (covering scenarios without caching). The historical reliable data cache uses a first-in, first-out (FIFO) mechanism. If reliable data exists, the most recent reliable data is retrieved from the cache. The interval between the current data and the reliable data is determined. If it is within a preset range, the data is confirmed to be valid and directly reused, as described in Embodiment 1. If it is outdated, the dynamic estimation mechanism is switched to; the specific dynamic estimation mechanism is as follows.

[0091] When the buffer has no valid historical data (such as when the vehicle is starting for the first time or when the buffer data has expired), the gradient is derived by working backward from the known power parameters. The core is the balance between driving torque and driving resistance. The motor output torque is transmitted to the wheels through the transmission system, and it needs to overcome the gravitational resistance, rolling resistance, and air resistance. The specific equations are as follows:

[0092]

[0093]

[0094]

[0095]

[0096] In the formula, The motor outputs torque in real time. To estimate the component of gravity, For rolling resistance, For air resistance, The rolling resistance coefficient, The air drag coefficient, The frontal area of ​​the vehicle body. air density, This refers to the real-time driving speed.

[0097] This embodiment abandons the sensor model assumption and uses ABS wheel speed sensor or CAN bus wheel speed data as the basis. It obtains the actual acceleration through differential calculation, directly reflecting the vehicle's real motion state, avoiding the influence of noise and model deviation. Accuracy is defined by relative deviation to avoid misjudgment caused by the magnitude of acceleration values. At the same time, the threshold for adapting to forklift types and operating scenarios is calibrated through multi-condition experiments to ensure the accuracy of the judgment criteria. Backup strategies are designed according to the priority of response speed and accuracy, prioritizing the reuse of historical reliable data. When there is no valid data, the slope is estimated through the driving resistance balance equation to ensure that control is not interrupted when the analysis results fail.

[0098] Therefore, this embodiment uses the actual motion state of the vehicle's wheel axles as a benchmark, calculates the actual acceleration through differential wheel speed signals, and constructs a reliability verification system based on the actual motion state. This enables accurate determination of the reliability of the analytical data and emergency control after failure, providing reliable data assurance for torque compensation of electric counterbalance forklifts on slopes. It avoids the errors and loss of control risks caused by traditional sensor-dependent models, solves the problem of traditional sensor-dependent models being susceptible to noise interference, ensures the reliability of the analytical driving acceleration and slope angle during slope driving, and thus ensures the safety and stability of torque compensation control.

[0099] Example 3

[0100] In the scenario of ramp driving for industrial vehicles such as electric counterbalance forklifts, accurate slope angle analysis is a core prerequisite for torque compensation control. However, dynamic changes in vehicle load can directly lead to vehicle posture deviation and sensor reference drift. For example, when a front-mounted forklift is fully loaded, the weight of the load is forward, which can cause the front of the forklift to sink, resulting in an angle between the acceleration sensor deployed on the vehicle body and the actual ramp plane. Similarly, when a counterbalance forklift is heavily loaded, the center of gravity shifts backward, which can also cause a deviation between the sensor measurement reference and the actual ramp angle. If this deviation is not corrected, it will lead to a significant error between the analyzed slope angle and the actual ramp angle, which in turn will cause inaccurate calculation of additional torque, potentially leading to safety risks such as insufficient power and rollback when going uphill, and excessive torque surge when going downhill.

[0101] This implementation example Figure 8 As shown, a load slope angle correction mechanism is introduced based on the original actual acceleration. The core logic is that changes in vehicle load cause a shift in the vehicle's attitude and sensor reference. A correction factor needs to be constructed using real-time load data to dynamically adjust the resolved slope angle, further improving data accuracy. Therefore, the purpose of this embodiment is to solve the problem of slope angle resolution deviation under different loads, especially for different types of forklifts, adapting to the differentiated impact of load on vehicle attitude; the correction factor is calculated based on vehicle basic parameters and real-time data, avoiding subjective experience-based corrections and ensuring a controllable correction process.

[0102] Based on pressure sensors deployed at the vehicle's load location, vehicle load data is collected in real time. A load slope angle correction factor is constructed based on the vehicle type, and the slope angle is corrected using the correction factor. This embodiment introduces pressure sensors deployed at the vehicle's load location to capture dynamic load change data in real time, constructs a load slope angle correction factor adapted to the vehicle type, and performs secondary optimization on the slope angle after reliability verification, eliminating load interference from the root and ensuring the accuracy of slope angle resolution and the safety of subsequent control.

[0103] The calculation process of the correction factor includes the following steps:

[0104] S1. Obtain basic vehicle parameters, including the vehicle's rated load capacity and maximum climbing angle; the above data are provided by the vehicle's factory or calibration data and serve as the basis for subsequent calculations.

[0105] S2. Calculate the load ratio and slope ratio; the load ratio is the ratio of real-time load data to rated load mass; the slope ratio is the ratio of the resolved slope angle to the maximum climbing angle; this embodiment calculates two core ratios based on real-time load data collected by pressure sensors and the resolved slope angle, respectively, to quantify the influence of real-time operating conditions on the slope angle. The specific calculation formulas are as follows:

[0106]

[0107] In the formula, The vehicle load data is collected in real time by a pressure sensor. For rated load mass, This represents the load ratio.

[0108]

[0109] In the formula, The slope angle that has been determined to be reliable after the original reliability verification is the analytical slope angle. This represents the maximum climbing angle. .

[0110] S3. The load ratio and slope ratio are combined to form the first influencing factor, and the load ratio is used as the second influencing factor. The first and second influencing factors are weighted and combined to form the correction factor. The core of the correction factor is to combine the dual influence of load and slope. The correction accuracy is taken into account under the combined working conditions by weighting the first influencing factor (load + slope combination) and the second influencing factor (pure load).

[0111] First, the first influence factor is calculated by multiplying the load ratio and the slope ratio to reflect the synergistic effect of load and slope (e.g., under high load and steep slope, the vehicle body posture deviation is more significant, requiring stronger correction). The first influence factor ranges from [0,1]. The greater the load and slope, the closer the first influence factor is to 1, and the stronger the correction requirement. The specific calculation formula is as follows:

[0112]

[0113] Then, the second influencing factor is calculated, using the load ratio as the second influencing factor, to separately reflect the basic impact of load on vehicle attitude (even on a flat road, a high load can cause sensor reference offset), the formula is:

[0114]

[0115]

[0116] This embodiment is... and Different weights are assigned, and these weights need to be calibrated according to the vehicle type. Correction factor. The physical meaning of is the proportional coefficient that the slope angle needs to be adjusted under the current working conditions. The slope angle will be corrected subsequently using the following correction formula:

[0117]

[0118] S4. Verify the validity of the obtained correction factor. If the correction factor exceeds the preset range, use the average correction factor from historical similar operating conditions as a temporary valid correction factor output and trigger an alarm. If the correction factor is within the preset range, output a valid correction factor. By constructing correction factors using real-time load data, the system dynamically corrects vehicle attitude deviation and sensor reference drift caused by load, effectively eliminating slope angle resolution deviations under different loads and vehicle types. The accuracy optimization effect is particularly significant under heavy loads and steep slopes, conditions prone to deviations. This makes the corrected slope angle more closely match the actual slope angle, resulting in more accurate subsequent additional torque calculations. It avoids safety risks such as insufficient uphill power causing vehicle slippage and excessive downhill torque fluctuations due to slope angle resolution deviations, thus improving vehicle safety on slopes.

[0119] This embodiment addresses the problem of slope angle resolution deviation caused by dynamic load changes during industrial vehicle ramp driving, which leads to vehicle attitude shift and sensor reference drift. Building upon the existing actual acceleration reliability verification system, a load slope angle correction mechanism is introduced. Specifically, pressure sensors deployed at the vehicle's load-bearing locations collect load data in real time. A load slope angle correction factor is constructed based on vehicle type characteristics, and the verified slope angle is then further optimized. This eliminates load interference at its source, ensuring slope angle resolution accuracy and providing reliable data support for subsequent torque compensation control.

[0120] The core objective of this embodiment is to solve the problem of slope angle analysis deviation under different loads and vehicle types, adapt to the different effects of load on vehicle body posture, and at the same time, quantitatively calculate the correction factor based on the vehicle's basic parameters and real-time data to avoid subjective experience correction and ensure that the correction process is scientific and controllable.

[0121] Example 4

[0122] In this embodiment, the load slope angle correction mechanism in Embodiment 2 has fixed weights for the first and second influencing factors, without being associated with the reliability of the analyzed slope angle. When the analyzed slope angle is determined to be reliable after reliability verification, the load interference on the slope angle is already small. The correction force under the fixed weight is too strong, which may lead to overcorrection, adjusting the already accurate slope angle to deviate from the actual value and introducing new deviations. When the analyzed slope angle is determined to be unreliable, the load interference and sensor reference drift are more significant. The correction force under the fixed weight is weak and cannot fully offset the deviation, resulting in a large gap between the corrected slope angle and the actual value, making it difficult to support subsequent torque compensation control.

[0123] This implementation example Figure 9 As shown, the weights of the first and second impact factors are adjusted based on the range of credibility. If the credibility is determined to be credible, the weights of the first and second impact factors are reduced; if the credibility is determined to be unreliable, the weights of the first and second impact factors are increased.

[0124] This embodiment first defines a credibility quantification index. Based on the original judgment logic of accuracy Z and threshold, the qualitative credibility and untrustworthiness are transformed into a quantitative credibility coefficient. The specific quantification formula is as follows:

[0125]

[0126] Based on the above formula, when it is determined to be credible, The value ranges from 0.5 to 1. When it is determined to be unreliable... The value ranges from 0 to 0.3; the confidence coefficient. Directly determines the direction of weight adjustment: The larger the value, the smaller the weight adjustment coefficient (reducing the weight of the impact factor). The smaller the value, the larger the weight adjustment coefficient (increasing the weight of the impact factor).

[0127] Based on credibility coefficient Introducing basic weights , In the original mechanism, the fixed weight for adapting to vehicle type is dynamically adjusted through a weight adjustment coefficient k. The specific quantification method of k is as follows:

[0128]

[0129]

[0130]

[0131] In implementation, and The sum is 1. If adjustments are made later, they can be corrected through normalization.

[0132] When the credibility is determined to be unreliable, the estimated slope value based on the vehicle longitudinal dynamics equation is introduced after correcting the initial slope angle with a correction factor. The estimated slope value and the corrected slope angle are then weighted and fused to obtain the final slope angle.

[0133] This embodiment, based on the weight adjustment of the influence factor of credibility and the slope fusion strategy for untrusted scenarios, is a deep optimization of the original load slope angle correction mechanism: the correction strength is dynamically adapted through credibility quantification, and the accuracy bottleneck of untrusted scenarios is solved through dual fusion. At the same time, this embodiment enables the slope angle analysis to maintain high accuracy in all credibility scenarios, providing more reliable data support for the slope torque compensation control of industrial vehicles such as electric counterbalance forklifts, and is especially suitable for scenarios such as logistics warehousing and port transshipment with large load fluctuations and complex slope conditions.

Claims

1. A method for rapidly acquiring slope information and vehicle operating status, characterized in that: Based on a first acceleration sensor set parallel to the vehicle's direction of travel and a second acceleration sensor set perpendicular to the first acceleration sensor, the first acceleration component along the slope direction and the second acceleration component perpendicular to the slope direction are measured synchronously. Based on the projection relationship between the second acceleration component and the gravitational acceleration in the direction perpendicular to the slope, the current slope angle is analyzed. Based on the relationship between the first acceleration component and the projection of gravitational acceleration along the slope, the vehicle's driving acceleration is analyzed. The obtained slope angle and driving acceleration are used to perform torque compensation control on the vehicle drive motor.

2. The method for rapidly obtaining slope information and vehicle operating status according to claim 1, characterized in that: The actual acceleration of the vehicle along the slope is obtained. The ratio of the difference between the actual acceleration and the driving acceleration to the actual acceleration is used as the accuracy. The accuracy is compared with a threshold to obtain the reliability.

3. The method for rapidly obtaining slope information and vehicle operating status according to claim 2, characterized in that: If the accuracy is lower than the threshold, the analysis result is deemed reliable and used for subsequent torque compensation control; if the accuracy is higher than or equal to the threshold, the analysis result is deemed unreliable and a backup control strategy is activated.

4. The method for rapidly obtaining slope information and vehicle operating status according to claim 3, characterized in that: The activation of the backup control strategy includes: discarding the current analysis result and using the most recently determined reliable slope angle and driving acceleration data for torque compensation control, or using slope information estimated based on the vehicle driving resistance balance equation for torque compensation control.

5. The method for rapidly obtaining slope information and vehicle operating status according to claim 1, characterized in that: The additional torque required to overcome the component of gravity in the slope direction is calculated based on the slope angle, and the additional torque is superimposed with the compensation torque determined based on the driver's required torque and the actual driving acceleration, as the total target torque output of the drive motor.

6. The method for rapidly obtaining slope information and vehicle operating status according to claim 2, characterized in that: Based on pressure sensors deployed at the vehicle's load location, vehicle load data is collected in real time. A load slope angle correction factor is constructed based on the vehicle type, and the slope angle is corrected using this correction factor. The calculation process of the correction factor includes the following steps: S1. Obtain basic vehicle parameters, including the vehicle's rated load capacity and maximum climbing angle; S2. Calculate the load ratio and slope ratio; the load ratio is the ratio of real-time load data to rated load mass; the slope ratio is the ratio of the analyzed slope angle to the maximum climbing angle. S3. Combine the load ratio and slope ratio to form the first influencing factor, and use the load ratio as the second influencing factor. Weight the first influencing factor and the second influencing factor to form the correction factor. S4. Verify the validity of the obtained correction factor. If the correction factor exceeds the preset range, call the average correction factor of the same working conditions in history as the temporary valid correction factor output and trigger an alarm. If the correction factor is within the preset range, then a valid correction factor is output.

7. The method for rapidly obtaining slope information and vehicle operating status according to claim 6, characterized in that: The weights of the first and second impact factors are adjusted based on the range of credibility.

8. The method for rapidly obtaining slope information and vehicle operating status according to claim 7, characterized in that: If the credibility is determined to be credible, the weights of the first and second impact factors are reduced; if the credibility is determined to be unreliable, the weights of the first and second impact factors are increased.

9. The method for rapidly obtaining slope information and vehicle operating status according to claim 6, characterized in that: When the credibility is determined to be unreliable, the estimated slope value based on the vehicle longitudinal dynamics equation is introduced after correcting the initial slope angle with a correction factor. The estimated slope value and the corrected slope angle are then weighted and fused to obtain the final slope angle.

10. The method for rapidly obtaining slope information and vehicle operating status according to claim 1, characterized in that: The raw signals output by the first and second accelerometers are filtered for noise reduction and outlier removal; the filtering and noise reduction are implemented using a discrete Kalman filter.

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