Mining vehicle gear shifting strategy based on multi-source key parameter interval uncertainty modeling and linear weighting comprehensive decision

Abstract

The invention discloses a mining vehicle gear shifting strategy based on multi-source key parameter interval uncertainty modeling and linear weighting comprehensive decision, which comprises the following steps of: arranging a load sensor, an acceleration sensor and an inclinometer in a whole vehicle hardware system; the vehicle control unit collects data collected by the load sensor, the acceleration sensor and the inclinometer in real time; a road surface friction coefficient calculation method, a gear shifting decision algorithm based on a friction coefficient, a gear shifting decision algorithm based on inclinometer data, a gear shifting decision algorithm based on a brake signal and a comprehensive decision algorithm based on interval uncertainty modeling are integrated in the vehicle control unit. And the vehicle control unit carries out interval uncertainty modeling analysis according to the obtained data and carries out comprehensive decision making according to an analysis result so as to determine a corresponding gear shifting decision. According to the method, the problem of shift strategy misalignment caused by insufficient multi-source information fusion is solved, and decision robustness is enhanced through interval uncertainty modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic transmission control for mining vehicles, and in particular to a shifting strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making. Background Art

[0002] Currently, the automatic shifting strategies for mining vehicles mainly rely on traditional speed - rotation speed threshold judgment methods, and such methods have obvious technical limitations. In the complex working environment of mines, the existing technical solutions are difficult to effectively cope with the changing road surface friction conditions, the steep slope conditions with drastic changes, and the special requirements of heavy-load transportation. The traditional shifting control system only relies on a single speed or rotation speed signal for decision-making, and can neither sense the real-time change of road surface adhesion nor accurately identify the slope state of the vehicle, resulting in frequent shifting misjudgments under wet road surface or steep slope conditions.

[0003] The core problems existing in the prior art are mainly reflected in four aspects: First, there is a lack of a multi-source information fusion mechanism, and it is impossible to comprehensively consider the influence of key parameters such as slope, load, and braking state on shifting decisions; second, there is a lack of effective monitoring means for the dynamic change of road surface friction coefficient, and it is difficult to adjust the shifting strategy in a timely manner when encountering low adhesion conditions; third, the braking condition is only treated as a simple protection condition, and the auxiliary deceleration function of engine braking in long downhill conditions cannot be fully utilized; finally, the existing algorithms cannot handle the uncertainty brought by sensor measurement errors and environmental parameter fluctuations, resulting in insufficient robustness of shifting decisions.

[0004] These problems are manifested in the actual operation of mining vehicles as safety hazards such as insufficient power in uphill conditions, decline in braking efficiency in downhill conditions, and slipping of driving wheels on wet road surfaces, and at the same time, mechanical shocks and decreased fuel economy caused by frequent shifting. Especially in heavy-load transportation scenarios, the traditional shifting strategy is difficult to balance power requirements and operation safety, seriously affecting the mining operation efficiency and the service life of equipment. Summary of the Invention

[0005] In view of this, the present invention provides a shifting strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making, which can improve the robustness of shifting decisions, optimize the balance between power performance and safety, and enhance the adaptability to complex working conditions.

[0006] To achieve the above object, the present invention provides the following technical solutions: A mine vehicle shifting strategy based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making, including: setting a load sensor, an acceleration sensor and an inclinometer in the vehicle hardware system, and the vehicle controller real-time collects the data collected by the load sensor, the acceleration sensor and the inclinometer; the vehicle controller integrates a road surface friction coefficient calculation method, a shifting decision-making algorithm based on the friction coefficient, a shifting decision-making algorithm based on the inclinometer data, a shifting decision-making algorithm based on the braking signal, and a comprehensive decision-making algorithm based on interval uncertainty modeling. The vehicle controller performs interval uncertainty modeling analysis according to the acquired data, and makes a comprehensive decision according to the analysis result to determine the corresponding shifting decision.

[0007] Preferably, the road surface friction coefficient calculation method is to calculate the coefficient through vehicle sensing data and mechanical models, and filtering and correction are used to improve the estimation accuracy, including: using various sensors installed on the vehicle to real-time obtain vehicle state parameters, calculating the longitudinal slip ratio of the tire according to the vehicle state parameters, and at the same time combining the vehicle dynamics relationship to calculate the friction force acting on the tire, and calculating the road surface friction coefficient according to the longitudinal slip ratio of the tire and the friction force acting on the tire , by continuously monitoring the longitudinal slip ratio of the tire and the longitudinal speed of the vehicle, when there is a tendency to slip, capture the friction force peak value and update the calculation of the road surface friction coefficient to make it approach the upper limit of the true road surface adhesion coefficient; the calculation formula of the longitudinal slip ratio of the tire is: ; in the formula, is the longitudinal slip ratio of the tire; is the rolling radius of the tire; is the angular velocity of the wheel; is the longitudinal speed of the vehicle.

[0008] Preferably, using various sensors installed on the vehicle to real-time obtain vehicle state parameters includes: using a wheel speed sensor to obtain the angular velocity of the wheel , using a vehicle speed sensor or GPS to obtain the longitudinal speed of the vehicle , using an accelerometer to obtain the vehicle acceleration , using the vehicle control unit to obtain the engine output torque and the current gearbox gear and transmission ratio information.

[0009] Preferably, the gearshift decision based on the friction coefficient includes: the gearshift strategy for high-friction coefficient road conditions and the gearshift strategy for low-friction coefficient road conditions. In the gearshift strategy for high-friction coefficient road conditions, the vehicle will delay upshifting to fully utilize the engine performance. At the same time, when accelerating to overtake or climb a slope, the vehicle will downshift in a timely manner to ensure power supply. In the gearshift strategy for low-friction coefficient road conditions, the vehicle will upshift in advance to reduce the torque on the wheels. At the same time, when decelerating or needing to downshift, the vehicle will delay downshifting to smooth out the torque change.

[0010] Preferably, the gearshift decision based on the inclinometer data includes: the vehicle control unit divides the road slope into three categories: uphill, downhill, and flat road according to the slope angle provided by the inclinometer. When the slope angle satisfies , the road slope is a flat road, and the normal gearshift strategy is enabled at this time. When the slope angle satisfies , the road slope is uphill, and the uphill gearshift strategy is enabled at this time. When the slope angle satisfies , the road slope is downhill, and the downhill gearshift strategy is enabled at this time.

[0011] Preferably, in the uphill gearshift strategy, the vehicle will delay upshifting to ensure power supply. At the same time, if the vehicle shows signs of deceleration or increased load during uphill driving, the vehicle will downshift in a timely manner to ensure that the engine always maintains sufficient torque output. In the downhill gearshift strategy, the vehicle will upshift in advance to improve fuel economy and driving smoothness. At the same time, if the vehicle speed gets out of control due to gravity during downhill driving, the vehicle will downshift in advance to control the vehicle's deceleration.

[0012] Preferably, the gearshift decision based on the brake signal includes: the vehicle control unit monitors the brake pedal signal of the vehicle in real time. When it detects that the brake pedal is depressed, the vehicle control unit determines that the vehicle is in the braking condition. During continuous braking, if it is found that the vehicle speed does not decrease significantly, the vehicle control unit determines that the current braking intensity is insufficient and triggers active downshifting and engine braking.

[0013] Preferably, when the active downshifting condition is met, the vehicle control unit will execute the downshifting operation in a timely manner to increase the engine speed, thereby introducing engine braking assistance. After the engine speed increases, the internal resistances such as the compression resistance and the intake and exhaust resistance generated during the compression stroke increase, exerting an additional reverse drag torque on the transmission system, enabling the driving wheels to obtain stronger braking force to assist in deceleration.

[0014] Preferably, the comprehensive decision-making algorithm based on interval uncertainty modeling includes: using intervals to represent the road surface friction coefficient , slope angle and brake signal The uncertainty range; construct a unified comprehensive cost function for each candidate gear: , where each is a weight coefficient; is the cost of the current gear in terms of safety, and its range is calculated through the uncertainty range of the road surface friction coefficient and the braking signal ; is the cost of the current gear in terms of power performance, and its range is calculated through the uncertainty range of the slope angle ; is the cost of the current gear in terms of economy, and its range is calculated through the uncertainty range of the slope angle ; is the cost of the current gear in terms of ride comfort, and its range is calculated through the uncertainty range of the slope angle and the braking signal ; The comprehensive cost function adopts the form of linear weighted summation, combining indicators such as safety, power performance, economy, and ride comfort together to evaluate the performance advantages and disadvantages of each candidate gear. The vehicle controller selects the optimal gear according to the evaluation results and signals to control the shift controller to shift gears.

[0015] Preferably, each candidate gear corresponds to a comprehensive cost range: , where , that is, the minimum cost in the optimistic case, , that is, the maximum cost in the pessimistic case. By linearly weighting and combining the sub-costs under various uncertainty factors, a unified evaluation of different gear schemes is realized; for each candidate gear, focus on the upper limit of its comprehensive cost range, and select the gear corresponding to the smallest one as the optimal gear, that is, when the algorithm does not know the exact environmental parameters, select the shift scheme that can still minimize the loss in the worst case.

[0016] The beneficial effects of the present invention are as follows: Compared with the prior art, the present application solves the problem of inaccurate shift strategy caused by insufficient multi-source information fusion, improves the response speed to the dynamic change of the road surface friction coefficient, effectively uses the braking signal to optimize the deceleration control, and enhances the decision-making robustness through interval uncertainty modeling. The hardware system cooperates with the software algorithm to form a closed-loop control, making the shift decision more in line with the actual working condition requirements, reducing power interruption and mechanical shock, and ensuring the safe operation of the mining vehicle in a complex environment.

[0017] The additional aspects and advantages of the present invention will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present invention. Brief Description of the Drawings

[0018] Figure 1 is a schematic diagram of the vehicle's hardware and software of the present invention; Figure 2 is a flowchart of the shift decision-making based on the friction coefficient of the present invention; Figure 3 is a flowchart of the shift decision-making based on the inclinometer data of the present invention; Figure 4 is a flowchart of the shift decision-making based on the braking signal of the present invention; Figure 5 is a flowchart of the comprehensive decision-making based on the interval uncertainty modeling of the present invention. Detailed Description of the Invention

[0019] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0020] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0021] The following refers to Figures 1 to 5 Describe the shift strategy of a mining vehicle based on interval uncertainty modeling and linear weighted comprehensive decision-making of multi-source key parameters in the embodiments of the present invention.

[0022] An embodiment of the present application discloses a shift strategy of a mining vehicle based on interval uncertainty modeling and linear weighted comprehensive decision-making of multi-source key parameters, including: setting a load sensor, an acceleration sensor and an inclinometer in the vehicle hardware system, and the vehicle controller collects the data collected by the load sensor, the acceleration sensor and the inclinometer in real time; the vehicle controller integrates a road surface friction coefficient calculation method, a shift decision-making algorithm based on the friction coefficient, a shift decision-making algorithm based on the inclinometer data, a shift decision-making algorithm based on the braking signal and a comprehensive decision-making algorithm based on interval uncertainty modeling. The vehicle controller performs interval uncertainty modeling analysis according to the acquired data and makes a comprehensive decision according to the analysis result to determine the corresponding shift decision.

[0023] Among them, the load sensor is used to monitor the change of vehicle load in real time, providing load fluctuation data for the shifting strategy, and its output signal can reflect the impact of the cargo box load on the power demand. The acceleration sensor is used to capture the dynamic characteristics of the vehicle's longitudinal acceleration, providing acceleration data input for the friction coefficient calculation. The inclinometer is used to measure the longitudinal slope angle of the vehicle, and its output signal is used to identify uphill or downhill working conditions. The interval uncertainty modeling quantifies the fluctuation range of sensor data into interval parameters, and comprehensively evaluates the gear performance boundary through a linear weighting function to form a robust decision-making basis.

[0024] Specifically, the vehicle controller collects load, acceleration, and slope data in real time through multi-source sensors, providing input for subsequent algorithms. The interval uncertainty modeling expands the measured values of each parameter into a fluctuation interval, constructs a comprehensive cost function to evaluate the worst and optimal performance of the gear in terms of safety, power, economy, and smoothness, and finally selects the gear with the minimum comprehensive cost to perform gear shifting.

[0025] Compared with the existing technology, the traditional scheme relies on a single parameter such as speed or rotation speed to trigger gear shifting, and cannot perceive the road surface adhesion state and slope change, which is prone to misjudgment under complex working conditions. This scheme obtains load, slope, and friction coefficient data through multi-source sensor fusion, forming multi-dimensional decision-making input, effectively improving the accuracy of working condition recognition. The interval modeling incorporates parameter fluctuations into the decision-making process, avoiding gear shifting jitter caused by instantaneous measurement errors and enhancing the robustness of the algorithm. The linear weighting comprehensive decision-making mechanism replaces the traditional threshold judgment to achieve multi-objective collaborative optimization.

[0026] Through the above technical solutions, this application solves the problem of inaccurate shifting strategy caused by insufficient multi-source information fusion, improves the response speed to the dynamic change of road surface friction coefficient, effectively utilizes the braking signal to optimize the deceleration control, and enhances the decision-making robustness through interval uncertainty modeling. The hardware system and software algorithm form a closed-loop control, making the gear shifting decision more in line with the actual working condition requirements, reducing power interruption and mechanical shock, and ensuring the safe operation of the mining truck in a complex environment.

[0027] In some embodiments, the method for calculating the road surface friction coefficient is to calculate the coefficient through vehicle sensing data and mechanical models, and use filtering and calibration to improve the estimation accuracy, including: using various sensors installed on the vehicle to obtain vehicle state parameters in real time, calculating the longitudinal slip ratio of the tire according to the vehicle state parameters, and at the same time combining the vehicle dynamics relationship to calculate the friction force acting on the tire, and calculating the road surface friction coefficient according to the longitudinal slip ratio of the tire and the friction force acting on the tire , by continuously monitoring the longitudinal slip ratio of the tire and the longitudinal speed of the vehicle, capturing the friction force peak value when there is a tendency of slipping, and updating the road surface friction coefficient Calculation is performed to approximate the upper limit of the actual road surface adhesion coefficient; various sensors installed on the vehicle are used to obtain vehicle state parameters in real time, including: obtaining the wheel angular velocity using a wheel speed sensor , obtaining the vehicle longitudinal speed using a vehicle speed sensor or GPS , obtaining the vehicle acceleration using an accelerometer , obtaining the engine output torque using the vehicle control unit and the current gear and transmission ratio information of the gearbox. The formula for calculating the longitudinal slip ratio of the tire is: ; in the formula, is the longitudinal slip ratio of the tire; is the rolling radius of the tire; is the wheel angular velocity; is the vehicle longitudinal speed.

[0028] Among them, the vehicle state parameters are dynamic information such as the wheel angular velocity, vehicle longitudinal speed, acceleration, and engine output torque collected by sensors in real time, and are the input data required to construct the tire mechanical model. Filtering and calibration are processing methods for suppressing noise and correcting errors in the original sensor data, eliminating the influence of instantaneous interference on the friction coefficient calculation, and improving the stability of the calculation results. The longitudinal slip ratio of the tire is the degree of difference between the actual linear speed and the theoretical linear speed of the tire, and is specifically calculated by the formula: It is used to quantify the tire slipping state and provide a dynamic basis for capturing the peak friction force. Capturing the peak friction force is a process of identifying the maximum friction force through the change trend of the slip ratio when the tire approaches the adhesion limit. Specifically, it can be achieved by monitoring the change rate of the slip ratio and combining the acceleration mutation, and dynamically updating the upper limit value of the friction coefficient.

[0029] Specifically, during vehicle driving, the wheel speed sensor and the vehicle speed sensor respectively collect the wheel angular velocity and the vehicle longitudinal speed, and calculate the slip ratio in combination with the rolling radius of the tire. When the absolute value of the slip ratio increases, it indicates that the tire enters the sliding state. At this time, the difference between the theoretical traction force and the measured net traction force is calculated through the engine output torque and acceleration data, and the real-time friction force between the tire and the ground is estimated. During the continuous monitoring of the slip ratio and the longitudinal speed, if it is detected that the slip ratio rises rapidly and the acceleration does not increase synchronously, it is determined that the tire has a slipping trend. At this time, the maximum friction force recorded is the peak value under the current adhesion conditions, and the road surface friction coefficient is updated accordingly. The original sensor data is smoothed through a filtering algorithm to eliminate instantaneous noise interference, and the calibrated friction coefficient is used as a key input parameter for gear shifting decisions.

[0030] Compared with the prior art, traditional methods rely on empirical judgment or indirect inference of the slip state, unable to accurately capture the friction force peak during the dynamic skidding process, resulting in a large lag or deviation in the estimation of the adhesion coefficient. This solution directly correlates sensor data with tire mechanical characteristics through a mechanical model, combines the real-time calculation of the slip ratio and the dynamic peak capture mechanism, can identify the adhesion limit at the initial stage of the skidding trend, and improves the calculation robustness under complex working conditions through filtering and correction.

[0031] Through the above technical solution, this application solves the problem of insufficient accuracy in estimating the road surface friction coefficient in the extreme environment of the mining area, overcomes the defect that the traditional method cannot update the upper limit of the adhesion coefficient in time during the transient skidding process, and realizes the accurate estimation of the friction coefficient through the sensor data fusion and the dynamic correction mechanism, providing a reliable basis for the gear shifting decision.

[0032] In some embodiments, the gear shifting decision based on the friction coefficient includes: the gear shifting strategy under high friction coefficient road conditions and the gear shifting strategy under low friction coefficient road conditions. In the gear shifting strategy under high friction coefficient road conditions, the vehicle will delay upshifting to give full play to the engine performance. At the same time, when accelerating to overtake or climb a slope, the vehicle will downshift in time to ensure power supply. In the gear shifting strategy under low friction coefficient road conditions, the vehicle will upshift in advance to reduce the torque on the wheels. At the same time, when decelerating or needing to downshift, the vehicle will delay downshifting to smooth the torque change.

[0033] Among them, delaying upshifting under high friction coefficient road conditions means raising the engine speed threshold required for upshifting to the upper limit of the preset range. For example, adjusting the upshifting trigger condition from the conventional 2000 revolutions per minute to above 2500 revolutions per minute, enabling the engine to continuously operate in the high torque range. This operation enables the transmission system to output the maximum traction force under high adhesion conditions. Advancing upshifting under low friction coefficient road conditions means lowering the upshifting trigger speed threshold to the lower limit of the preset range. For example, adjusting from the conventional 1800 revolutions per minute to 1500 revolutions per minute, restricting the amplitude of the torque output on the wheels by reducing the gear ratio. Delaying downshifting means setting the downshifting trigger vehicle speed threshold to a lower level than the conventional working condition during braking or decelerating. For example, reducing 5 - 10 kilometers per hour based on the conventional downshifting vehicle speed, suppressing the torque mutation by slowing down the gear shifting frequency.

[0034] Specifically, under high coefficient of friction road conditions, the value of the road surface friction coefficient obtained in real time is input into the shift decision-making module. When the friction coefficient exceeds the set threshold, the engine is allowed to operate in the peak power output range. At this time, if it is detected that the throttle opening suddenly increases or the vehicle acceleration is insufficient, the vehicle control unit immediately generates a downshift command to make the transmission shift into a lower gear to increase the torque on the wheels. Under low coefficient of friction road conditions, when the friction coefficient is lower than the threshold, the upshift speed threshold moves down to reduce the torque output fluctuation. At the same time, the downshift trigger condition during braking is delayed, and the downshift operation is only performed when the vehicle speed drops below the preset safety threshold, avoiding wheel slip caused by sudden gear changes.

[0035] Through the above technical solutions, the present application solves the problems of limited power output under high adhesion road conditions and mismatch between traction and adhesion under low adhesion road conditions. Under high friction conditions, delaying upshifting allows the engine torque to be fully converted into effective traction, improving the climbing ability of heavy-load transportation; timely downshifting ensures the power response speed under emergency acceleration conditions. Under low friction conditions, upshifting in advance suppresses the peak torque on the wheels, avoiding the tire breaking through the adhesion limit; delaying downshifting reduces the torque step during gear shifting, maintaining the dynamic balance between the driving wheels and the ground. This two-way adjustment mechanism realizes the coordinated optimization of power performance and stability under different adhesion conditions.

[0036] In some embodiments, the shift decision based on the inclinometer data includes: the vehicle control unit divides the road slope into three categories: uphill, downhill, and flat road according to the slope angle provided by the inclinometer When the slope angle satisfies , the road slope is a flat road, and at this time, the normal shift strategy is enabled. When the slope angle satisfies , the road slope is uphill, and at this time, the uphill shift strategy is enabled. When the slope angle satisfies , the road slope is downhill, and at this time, the downhill shift strategy is enabled. In the uphill shift strategy, the vehicle will delay upshifting to ensure power supply. At the same time, if the vehicle shows signs of deceleration or increased load during uphill driving, the vehicle will downshift in time to ensure that the engine always maintains sufficient torque output; in the downhill shift strategy, the vehicle will upshift in advance to improve fuel economy and driving smoothness. At the same time, if the vehicle speed gets out of control due to gravity during downhill driving, the vehicle will downshift in advance to control the vehicle deceleration.

[0037] Among them, the slope angle The division threshold is set at plus or minus 5 degrees, which refers to the critical angle for dividing the vehicle's longitudinal gradient into three typical working conditions. Specifically, it can be achieved by comparing the output signal of the inclinometer with the preset calibration value of the controller. This threshold balances the sensor measurement error and the sensitivity of working condition classification, preventing frequent switching of strategies caused by minor angle fluctuations. The inclinometer's real-time acquisition of the vehicle's longitudinal tilt angle data means continuously monitoring the vehicle's attitude through the inertial measurement unit installed on the vehicle body. Specifically, it can be achieved by using the fusion algorithm of the triaxial accelerometer and the gyroscope to ensure the dynamic accuracy of the gradient information. Delayed upshifting means setting the engine speed or vehicle speed threshold required for upshifting higher than the standard value under flat road conditions. Specifically, it can be achieved by dynamically adjusting the speed threshold, enabling the vehicle to maintain a low gear when going uphill to output higher torque. Timely downshifting means that when detecting a decrease in vehicle speed or a sudden increase in the load of the driving wheels, the downshifting trigger condition is relaxed to a lower speed or engine speed threshold than on flat roads, avoiding insufficient power due to too high a gear. Early upshifting means setting the engine speed or vehicle speed threshold required for upshifting lower than the standard value under flat road conditions, enabling the vehicle to shift into a higher gear as early as possible when going downhill to reduce the engine load. Early downshifting means that when detecting that the vehicle speed exceeds the safety threshold or the brake pedal is not depressed, the downshifting trigger condition is tightened to a higher speed or engine speed threshold than on flat roads, using the engine braking effect to assist in controlling the vehicle speed.

[0038] Specifically, the vehicle controller divides the vehicle driving conditions into three modes: flat road, uphill, and downhill by analyzing the gradient angle output by the inclinometer. When the detected gradient angle is within the range of plus or minus 5 degrees, the system determines it as a flat road condition and executes the standard shift logic, taking into account both fuel economy and driving smoothness at this time. When the gradient angle exceeds +5 degrees, the controller activates the uphill shift mode, maintaining the engine in the high-power output range by increasing the upshift speed threshold, and shortening the downshift response time to quickly shift into a lower gear when the load suddenly increases. When the gradient angle is lower than -5 degrees, the controller switches to the downhill shift mode, performing the upshift operation in advance to reduce the torque load on the transmission system, and actively downshifting to introduce engine braking when detecting abnormal vehicle speed increase. The independent operating mechanisms of the three types of shift strategies avoid the problem of insufficient adaptability of the traditional single shift logic under complex ramp conditions.

[0039] Compared with the prior art, the existing automatic shift system only performs gear shifting based on vehicle speed and engine speed, without establishing the gradient angle The association mechanism with the gear shift strategy. The traditional method is prone to power interruption due to failure to downshift in time when going uphill, and excessive braking or loss of speed control due to improper gear selection when going downhill. This solution obtains road slope information in real time through the inclinometer, and formulates three gear shift strategies in a targeted manner, so that the gearbox can dynamically adjust the gear shift threshold and response logic according to the actual slope conditions, solving the inherent defect that a single gear shift logic cannot adapt to slope changes.

[0040] Through the above technical solutions, this application effectively solves the problem of insufficient power caused by too high a gear in uphill conditions, and ensures that the engine continues to output sufficient torque by delaying upshifts; at the same time, it overcomes the inertial impact caused by too low a gear in downhill conditions, and uses early upshifts to reduce the load on the transmission system and optimize energy management. The slope angle threshold division mechanism suppresses misjudgment caused by sensor noise, and the independent operation of the three strategies ensures the accuracy of gear shifting operations under different slope conditions, achieving the coordinated optimization of power output and driving safety in complex mountain road environments.

[0041] In some embodiments, the shift decision based on the brake signal includes: the vehicle controller monitors the vehicle's brake pedal signal in real time. When it detects that the brake pedal is stepped on, the vehicle controller determines that the vehicle is in a braking condition. During continuous braking, if it is found that the vehicle speed has not dropped significantly, the vehicle controller determines that the current braking intensity is insufficient and triggers active downshifting and engine braking. When the conditions for active downshifting are met, the vehicle controller will perform the downshift operation in a timely manner to increase the engine speed, thereby introducing engine braking assistance. After the engine speed increases, the compression resistance generated by its compression stroke and internal resistances such as intake and exhaust resistance increase, applying additional anti-drag torque to the transmission system, so that the drive wheels obtain stronger braking force to assist in deceleration.

[0042] Among them, the brake pedal signal is an electrical signal output by the vehicle's braking system, which is used to characterize whether the driver has performed a braking operation. This signal serves as a trigger condition for judging whether the vehicle has entered a braking condition, and provides basic input for subsequent decision-making. Insufficient braking intensity means that the vehicle's deceleration effect has not met expectations. Specifically, the vehicle speed sensor can be used to collect the speed change rate in real time, and this can be achieved by comparing whether the speed difference before and after braking is lower than the preset threshold, which is used to identify the critical state of reduced efficiency of the friction brake system. Active downshifting is the control unit autonomously performing a shift operation. Specifically, the transmission controller can be used to force switching to a low gear, and the engine speed can be passively increased by increasing the transmission ratio, thereby changing the power transmission path. Engine brake assist uses the engine's running resistance to generate a reverse drag torque. Specifically, the engine can be forced to run at a high speed by lowering the gear, so that the gas resistance generated by the compression stroke and the fluid resistance of the intake and exhaust system increase, and the kinetic energy is converted into heat energy consumption.

[0043] Specifically, when the brake pedal signal is continuously activated and the vehicle speed does not decrease as expected, the control unit determines that the current frictional braking force is insufficient. At this time, the system triggers a downshift command, and the transmission actuator switches the gear to a lower gear. The increased transmission ratio causes the engine speed to increase passively. During the compression stroke of the engine, the gas pressure in the cylinder increases, and the piston movement needs to overcome greater resistance. At the same time, the high-speed rotating crankshaft drives the intake and exhaust valves to open and close frequently, exacerbating the turbulent losses of the intake manifold and exhaust back pressure. The counter-dragging torque formed by these internal resistances is transmitted in the reverse direction through the powertrain to the drive wheels, generating an additional braking torque. This braking torque is superimposed on the frictional braking force and acts together on the wheels to achieve a deceleration effect.

[0044] Through the above technical solution, the present application effectively solves the problem of difficult deceleration of mining vehicles due to insufficient frictional braking force under braking conditions. When it is detected that the brake pedal is activated and the vehicle speed does not decrease reasonably, the system forcibly increases the engine speed by actively downshifting, and uses the counter-dragging torque generated by its internal resistance to enhance the wheel braking force, thereby providing auxiliary braking force when the frictional braking system is overloaded or its efficiency decreases. This technical solution not only alleviates the risk of thermal decay of traditional frictional braking devices under long downhill conditions, but also improves the deceleration reliability through the coordinated control of the powertrain and the braking system, significantly enhancing the braking safety of heavy-duty mining trucks under complex road conditions.

[0045] In some embodiments, the comprehensive decision-making algorithm based on interval uncertainty modeling includes: using intervals to represent the uncertain ranges of road surface friction coefficient , slope angle and braking signal ; constructing a unified comprehensive cost function for each candidate gear: , where each is a weight coefficient; is the cost of the current gear in terms of safety, and its interval is calculated through the uncertain ranges of road surface friction coefficient and braking signal ; is the cost of the current gear in terms of power performance, and its interval is calculated through the uncertain range of slope angle ; is the cost of the current gear in terms of economy, and its interval is calculated through the uncertain range of slope angle ; is the cost of the current gear in terms of ride comfort, and its interval is calculated through the slope angle and braking signal Calculate the uncertainty range; the comprehensive cost function adopts the form of linear weighted summation, integrating indicators such as safety, power performance, economy, and smoothness to evaluate the performance of each candidate gear. The vehicle controller selects the optimal gear according to the evaluation results and signals to control the shift controller to perform gear shifting.

[0046] Furthermore, each candidate gear corresponds to a comprehensive cost interval: , where , that is, the minimum cost in the optimistic case, , that is, the maximum cost in the pessimistic case. By linearly weighting and combining the sub-costs under various uncertainty factors, the unified evaluation of different gear schemes is realized; for each candidate gear, focus on the upper limit of its comprehensive cost interval, and select the gear corresponding to the smallest one as the optimal gear. That is, in the case of unknown precise environmental parameters, the algorithm selects the gear-shifting scheme that can still minimize the loss in the worst case.

[0047] Among them, interval uncertainty modeling refers to representing the fluctuation range of the real-time parameters collected by the sensor in the form of an interval. Specifically, it can be realized by using the maximum and minimum values within a set time window as the upper and lower bounds, which is used to quantify the parameter uncertainty caused by environmental dynamic changes and measurement errors. The comprehensive cost function refers to transforming multi-dimensional performance indicators into a mathematical evaluation model. Specifically, it can be realized by linearly weighting and summing to combine each sub-cost item into a single value, which is used to uniformly compare the comprehensive performance of different gears under complex working conditions. The weight coefficient refers to the proportional parameter reflecting the priority relationship of each performance indicator. Specifically, it can be realized by setting a fixed value through calibration experiments or expert experience, which is used to dynamically adjust the focus of the gear-shifting strategy. The maximin criterion refers to selecting the scheme with the minimum loss in the worst case under uncertain environment. Specifically, it can be realized by preferentially comparing the upper limit values of the comprehensive cost intervals of each gear, which is used to ensure the feasibility of the decision-making result under extreme parameter combinations.

[0048] Specifically, the algorithm first takes the extreme values of the road surface friction coefficient , slope angle and braking signal within a set time window as interval inputs. For example, set the interval of the road surface friction coefficient as [0.2, 0.8], and the slope angle The interval is set to [-15°, +20°]. For each candidate gear, the range of the four sub-costs of safety, power, economy and smoothness are calculated respectively: the safety cost interval is calculated by the lower limit of the friction coefficient and the trigger state of the brake signal. For example, the safety cost reaches the upper limit when the friction is low and the braking is continuous; the power cost interval is determined according to the maximum driving force demand corresponding to the upper limit of the slope angle. For example, the power cost of the high gear increases significantly under steep slope conditions; the economic cost interval is composed of the optimal value of the economy in the sliding condition corresponding to the lower limit of the slope angle and the conventional value on the flat road; the smoothness cost interval combines the slope change rate and the switching frequency of the brake signal to evaluate the gear shift shock risk. After the sub-cost intervals are linearly superimposed according to the preset weights, the comprehensive cost interval of each gear is generated. For example, the total cost interval of a gear is [3.2, 8.5]. By comparing the upper limits of the comprehensive cost intervals of all candidate gears, the gear with the smallest value is selected as the optimal decision. For example, when the cost upper limits of the three gears are 8.5, 9.1, and 7.9 respectively, the third gear is selected to perform the gear shift operation.

[0049] In some specific embodiments, the weight coefficient can be dynamically adjusted according to the vehicle operation mode. For example, in the safety priority mode, the safety weight is set to 0.5, and the other indicators are each 0.16; in the economy mode, the economy weight is increased to 0.4. The brake signal interval can be determined by counting the duration of pedal depression. For example, if more than 3 brake signals are detected within a 5-second time window, the brake state interval is set to [0,1]. The slope angle interval can be processed by sliding window filtering inclinometer data, for example, taking the maximum deviation of the slope angle within the past 2 seconds ±3° as the current interval range.

[0050] Compared with the existing technology, the traditional shift strategy only makes threshold judgments based on single-point estimates, which cannot handle the decision bias caused by sensor noise and parameter fluctuations. This solution converts parameter uncertainty into mathematically manageable upper and lower bounds through interval modeling, which enhances the algorithm's tolerance to measurement errors. Conventional methods use fixed priorities to deal with multi-objective conflicts, such as always giving priority to power and ignoring economy. This solution achieves a dynamic balance of four indicators through a linear weighting mechanism, and can automatically adjust the optimization target according to changes in working conditions.

[0051] Through the above technical solution, this application effectively solves the problem of insufficient robustness of gear shifting decisions of mining vehicles under multi-source parameter fluctuations, quantifies the uncertainty impact of environmental factors through interval analysis, and uses comprehensive cost functions to achieve multi-objective optimization, balancing power output and fuel economy while ensuring safety, while reducing the mechanical impact caused by frequent gear shifting. This solution can adapt to complex working conditions such as sudden changes in road friction coefficient, continuous changes in slope, and frequent triggering of brake signals, avoiding misjudgments caused by instantaneous abnormalities of a single parameter, and ensuring that the vehicle can still perform reasonable gear shifting operations under the most unfavorable environmental conditions.

[0052] For other components and operations of the shifting strategy of mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to the embodiments of the present invention, those skilled in the art are familiar with them and will not be described in detail herein.

[0053] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0054] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A shifting strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making, characterized in that Including: A load sensor, an acceleration sensor and an inclinometer are set in the vehicle hardware system, and the vehicle controller collects the data collected by the load sensor, the acceleration sensor and the inclinometer in real time; the vehicle controller integrates a road surface friction coefficient calculation method, a shift decision-making algorithm based on the friction coefficient, a shift decision-making algorithm based on the inclinometer data, a shift decision-making algorithm based on the braking signal and a comprehensive decision-making algorithm based on interval uncertainty modeling. The vehicle controller performs interval uncertainty modeling analysis according to the acquired data and makes a comprehensive decision according to the analysis result to determine the corresponding shift decision.

2. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 1, wherein The method for calculating the road surface friction coefficient is to calculate the coefficient through vehicle sensing data and mechanical models, and use filtering and correction to improve the estimation accuracy, including: using various sensors installed on the vehicle to obtain vehicle state parameters in real time, calculating the longitudinal slip ratio of the tire according to the vehicle state parameters, and at the same time calculating the frictional force acting on the tire in combination with the vehicle dynamics relationship, and calculating the road surface friction coefficient according to the longitudinal slip ratio of the tire and the frictional force acting on the tire , by continuously monitoring the longitudinal slip ratio of the tire and the longitudinal speed of the vehicle, capturing the peak value of the frictional force when there is a tendency to skid, and updating the calculation of the road surface friction coefficient to make it approach the upper limit of the true road surface adhesion coefficient; The calculation formula for the longitudinal slip ratio of the tire is as follows: ; In the formula, is the longitudinal slip ratio of the tire; is the rolling radius of the tire; is the angular velocity of the wheel; is the longitudinal speed of the vehicle.

3. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 2, characterized in that, Real-time obtain vehicle state parameters by using various sensors installed on the vehicle, including: obtain the wheel angular velocity by using a wheel speed sensor , obtain the vehicle longitudinal speed by using a vehicle speed sensor or GPS , obtain the vehicle acceleration by using an accelerometer , obtain the engine output torque by using a vehicle control unit and the current gearbox gear position and transmission ratio information.

4. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 1, wherein The shift decision based on the friction coefficient includes: the shift strategy under high friction coefficient road conditions and the shift strategy under low friction coefficient road conditions. In the shift strategy under high friction coefficient road conditions, the vehicle will delay upshifting to give full play to the engine performance. At the same time, when accelerating to overtake or climbing a slope, the vehicle will downshift in time to ensure power supply. In the shift strategy under low friction coefficient road conditions, the vehicle will upshift in advance to reduce the torque on the wheels. At the same time, when decelerating or needing to downshift, the vehicle will delay downshifting to smooth the torque change.

5. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 1, wherein The shift decision based on the inclinometer data includes: the vehicle controller divides the road gradient into three categories: uphill, downhill, and flat road according to the gradient angle provided by the inclinometer When the road gradient meets the road gradient is a flat road, and the normal shift strategy is enabled at this time. When the gradient angle meets the road gradient is uphill, and the uphill shift strategy is enabled at this time. When the gradient angle meets the road gradient is downhill, and the downhill shift strategy is enabled at this time.

6. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 5, characterized in that, In the uphill shift strategy, the vehicle will delay upshifting to ensure power supply. At the same time, if the vehicle shows signs of deceleration or increased load during uphill driving, the vehicle will downshift in time to ensure that the engine always maintains sufficient torque output; in the downhill shift strategy, the vehicle will upshift in advance to improve fuel economy and driving smoothness. At the same time, if the vehicle speed gets out of control due to gravity during downhill driving, the vehicle will downshift in advance to control the vehicle deceleration.

7. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 1, characterized in that, The shift decision based on the braking signal includes: the vehicle controller monitors the braking pedal signal of the vehicle in real time. When it detects that the brake pedal is depressed, the vehicle controller determines that the vehicle is in the braking condition. During continuous braking, if it is found that the vehicle speed does not decrease significantly, the vehicle controller determines that the current braking intensity is insufficient and triggers active downshifting and engine braking.

8. The mining vehicle shifting strategy based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 7, characterized in that, When the active downshifting condition is met, the vehicle controller will execute the downshifting operation in time to increase the engine speed, thereby introducing engine braking assistance; after the engine speed increases, the internal resistances such as the compression resistance generated during the compression stroke and the intake and exhaust resistance increase, applying an additional reverse drag torque to the transmission system, so that the driving wheels obtain stronger braking force to assist deceleration.

9. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 1, characterized in that The comprehensive decision-making algorithm based on interval uncertainty modeling includes: using intervals to represent the uncertainty ranges of road surface friction coefficient , slope angle , and braking signal ; constructing a unified comprehensive cost function for each candidate gear: , where each is a weight coefficient; is the cost of the current gear in terms of safety, and its interval is calculated through the uncertainty ranges of road surface friction coefficient and braking signal ; is the cost of the current gear in terms of power performance, and its interval is calculated through the uncertainty range of slope angle ; is the cost of the current gear in terms of economy, and its interval is calculated through the uncertainty range of slope angle ; is the cost of the current gear in terms of ride comfort, and its interval is calculated through the uncertainty ranges of slope angle and braking signal ; The comprehensive cost function adopts the form of linear weighted summation, combines indicators such as safety, power performance, economy and smoothness together to evaluate the performance of each candidate gear. The vehicle controller selects the optimal gear according to the evaluation result and signals to control the shift controller to shift gears.

10. The shift strategy for mining vehicles based on multi-source key parameter interval uncertainty modeling and linear weighted comprehensive decision-making according to claim 9, characterized in that Each candidate gear corresponds to a comprehensive cost range: , where , that is, the minimum cost in the optimistic case, , that is, the maximum cost in the pessimistic case. By linearly weighting and combining the sub-costs under various uncertainty factors, a unified evaluation of different gear schemes is achieved; for each candidate gear, focus on the upper limit of its comprehensive cost range, and select the gear corresponding to the smallest one as the optimal gear. That is, when the algorithm does not know the exact environmental parameters, it selects the gear-shifting scheme that can still minimize the loss in the worst case.

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