Slope travel control method and system for unmanned electric vehicle

Abstract

The application provides a slope driving control method and system of an unmanned electric vehicle, relates to the technical field of electric vehicles, and comprises the following steps: measuring the slope angle of vehicle driving in real time through an inertial measurement unit to monitor the unmanned electric vehicle; synchronizing a plurality of electric vehicle monitoring data to a single rudder wheel slope driving dynamics model to obtain a slope driving parameter set; activating an inductive coupling module and a direct current voltage control module to cooperatively control the operation of the unmanned electric vehicle, generating a hierarchical braking control strategy, and intelligently controlling the slope driving of the unmanned electric vehicle. The application solves the technical problem of low slope driving control precision in the prior art, i.e., the phenomenon of easy hill sliding when the unmanned electric vehicle is stopped and started on an uphill slope, and the phenomenon of speed out of control and downhill rush when the unmanned electric vehicle is on a downhill slope. The hierarchical braking control strategy is determined by combining the inductive coupling module and the direct current voltage control module, thereby improving the slope driving control precision.

Description

Technical Field

[0001] This application relates to the field of electric vehicle technology, specifically to a method and system for controlling ramp driving of driverless electric vehicles. Background Technology

[0002] Driverless electric vehicles, especially those with heavy-duty single-steering-wheel structures, face severe challenges in navigating in complex environments such as ports and logistics parks. Existing technologies exhibit significant shortcomings when handling slopes ≥15%, slope lengths ≥10 meters, and loads ≥2 tons: Because electromagnetic brakes are rigid, when the vehicle is stopped uphill, it relies on the friction between the brake pads and discs to balance the gravitational force. When the vehicle is fully loaded and the slope is steep, the static friction force on the brakes increases significantly, easily leading to overheating and wear of the brake pads or brake force attenuation. During startup, the motor needs to output sufficient torque within a short time to overcome static friction and the gravitational force. If the control strategy lags behind, the vehicle may briefly roll backward (0.5-1 meter), posing a safety hazard. When descending a slope, the component of the vehicle's gravity along the slope is converted into additional rotational speed of the drive wheels; existing motor controllers mostly employ regenerative braking or reverse braking modes. However, in long slope scenarios, the regenerative braking system is prone to triggering protection due to battery overcharging or insufficient heat dissipation, resulting in limited braking power. If the force of the vehicle going downhill exceeds the braking force of the electromagnetic brake, it may eventually lead to loss of control and lurching downwards. Some technologies improve braking force by adding a mechanical parking brake or adopting a dual-motor redundancy design, but these have the following problems: the mechanical parking device requires manual operation, which cannot meet the requirements in autonomous driving scenarios; the dual-motor system increases the weight and energy consumption of the vehicle, increasing the overall cost, and results in lower driving control accuracy on dynamic slopes, thus affecting driving safety.

[0003] In summary, existing technologies suffer from technical problems such as the tendency for driverless electric vehicles to slip when stopping and starting uphill, and to lose speed control and surge downhill when going downhill. These issues result in low accuracy of slope driving control and further affect driving safety. Summary of the Invention

[0004] The purpose of this application is to provide a slope driving control method and system for driverless trolleys, in order to solve the technical problems in the prior art where driverless trolleys are prone to slipping when stopping and starting on uphill slopes, and prone to speed loss and downward rushing when going downhill, resulting in low slope driving control accuracy and further affecting driving safety.

[0005] To achieve the above objectives, this application provides a method and system for controlling the ramp travel of an unmanned trolley.

[0006] Firstly, this application provides a slope driving control method for an unmanned trolley. This method is implemented through a slope driving control system for the unmanned trolley, comprising: measuring the slope angle of the vehicle in real time using an inertial measurement unit; monitoring the unmanned trolley based on the slope angle to obtain multiple trolley monitoring data; constructing a single-steering wheel slope driving dynamics model; synchronizing the multiple trolley monitoring data to the single-steering wheel slope driving dynamics model to obtain a slope driving parameter set; and, according to the slope driving parameter set, activating an inductive coupling module and a DC voltage control module to collaboratively control the operation of the unmanned trolley, generating a graded braking control strategy, and executing the graded braking control strategy to intelligently control the slope driving of the unmanned trolley.

[0007] Optionally, the inertial measurement unit performs real-time speed measurement on the driverless trolley according to the sampling frequency to obtain a vehicle speed dataset; the attitude of the driverless trolley is calculated according to the vehicle speed dataset to obtain the vehicle's horizontal pitch angle; the vehicle's horizontal pitch angle is used as the driving slope angle value of the driverless trolley, and a slope driving monitoring mode is activated based on the driving slope angle value; the driverless trolley is monitored based on the slope driving monitoring mode to obtain multiple trolley monitoring data.

[0008] Optionally, a multi-parameter fusion dynamics training is performed based on the multiple trolley monitoring data to construct a single-steering wheel ramp driving dynamics model; the multiple trolley monitoring data are analyzed to obtain vehicle speed data, steering wheel motor speed data, motor current data, and vehicle load mass data; the ramp angle value and the vehicle load mass data are used as input parameters and synchronized to the single-steering wheel ramp driving dynamics model for updating to obtain initial driving parameters; the vehicle speed data, steering wheel motor speed data, and motor current data are used as state feedback parameters, and closed-loop correction is performed on the initial driving parameters based on the state feedback parameters to generate the ramp driving parameter set.

[0009] Optionally, the following steps are taken: Retrieve historical slope driving datasets of the autonomous trolley, which include vehicle driving data at multiple slopes; perform load center change analysis on the autonomous trolley based on the vehicle driving data at multiple slopes to obtain vehicle mass distribution parameters; estimate friction force on the autonomous trolley at different slopes based on the vehicle driving data at multiple slopes to obtain tire-road contact friction force parameters; perform operation analysis on the motor of the autonomous trolley based on the vehicle driving data at multiple slopes to obtain motor characteristic parameters; perform power generation analysis on the motor of the autonomous trolley based on the vehicle driving data at multiple slopes to obtain motor energy feedback parameters; fuse the vehicle mass distribution parameters, the tire-road contact friction force parameters, the motor characteristic parameters, and the motor energy feedback parameters for training to construct a multi-dimensional coupling parameter set; and perform dynamic training and verification based on the multi-dimensional coupling parameter set to construct the single-steering wheel slope driving dynamic model.

[0010] Optionally, the driving slope angle value is input into the single-steering wheel slope driving dynamics model for slope calculation to obtain the gravity component parameters of the driverless trolley; the vehicle load mass data is input into the single-steering wheel slope driving dynamics model for mass calculation to obtain the vehicle inertia parameters of the driverless trolley; the single-steering wheel slope driving dynamics model is updated based on the gravity component parameters and the vehicle inertia parameters for force analysis to obtain the force analysis results; and the driving calculation of the driverless trolley is performed based on the force analysis results to obtain the initial driving parameters.

[0011] Optionally, an error assessment mechanism is constructed, comprising a first error threshold, a second error threshold, and a third error threshold; an error calculation is performed based on the vehicle mass distribution parameters and the vehicle load mass data to obtain a first error value, which is then compared with the first error threshold; when the first error value is greater than the first error threshold, a first state feedback parameter is generated to correct the tire-road contact friction parameter, generating a first correction parameter; an error calculation is performed based on the steering wheel motor speed data and the motor characteristic parameters to obtain a second error value, which is then compared with the second error threshold; when the second error value is greater than the third error threshold, a third error threshold is generated ... When the threshold is reached, a second state feedback parameter is generated to correct the torque constant of the motor characteristic parameter, generating a second correction parameter; based on the motor current value data and the motor energy feedback parameter, an error calculation is performed to obtain a third error value, which is compared with the third error threshold; when the third error value is greater than the third error threshold, a third state feedback parameter is generated to correct the motor equivalent resistance parameter of the motor energy feedback parameter, generating a third correction parameter; based on the first correction parameter, the second correction parameter, and the third correction parameter, combined with the vehicle driving speed data, the driving of the driverless electric vehicle is integrated to generate the slope driving parameter set.

[0012] Optionally, operating condition identification is performed based on the slope driving parameter set to determine multiple slope operating condition types; the inductive coupling module and the DC voltage control module are activated according to the multiple slope operating condition types to perform braking classification on the driverless trolley and define multiple braking levels; the operation control analysis of the driverless trolley is performed according to the multiple braking levels to construct a graded braking control strategy; and the graded braking control strategy is executed based on the inductive coupling module and the DC voltage control module.

[0013] Optionally, based on the multiple slope operating conditions, uphill and downhill operating conditions are obtained through analysis. When the slope operating condition of the autonomous trolley is the uphill condition, the inductive coupling module is activated to perform instantaneous torque compensation, obtaining the anti-rollover braking level and normal driving level. When the slope operating condition of the autonomous trolley is the downhill condition, the DC voltage control module is activated to perform braking classification, obtaining the light braking level, moderate braking level, and emergency braking level. Based on the inductive coupling module and the DC voltage control module, the autonomous trolley is subjected to cooperative driving analysis, generating cooperative driving analysis results. According to the cooperative driving analysis results, braking analysis is performed on the anti-rollover braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level, and an execution priority sequence is set according to the braking analysis results. The anti-rollover braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level are sorted according to the execution priority sequence to define the multiple braking levels.

[0014] Optionally, based on the anti-runaway braking level, the power switch of the inductive coupling module is alternately closed and opened to obtain anti-runaway torque control parameters; based on the mild braking level, the discharge current parameter of the DC voltage control module is limited to obtain a first braking control parameter; based on the moderate braking level, the operating frequency parameter of the inductive coupling module and the discharge current parameter of the DC voltage control module are coordinated to obtain a second braking control parameter; based on the emergency braking level, the maximum coupling degree of the inductive coupling module and the discharge current of the DC voltage control module are simultaneously activated to obtain a third braking control parameter; the first braking control parameter, the second braking control parameter, and the third braking control parameter are added to the graded braking control strategy.

[0015] Secondly, this application also provides a ramp driving control system for an unmanned trolley, used to execute the ramp driving control method for an unmanned trolley as described in the first aspect. The ramp driving control system includes: a ramp monitoring module, used to measure the ramp angle of the vehicle in real time using an inertial measurement unit, monitor the unmanned trolley based on the ramp angle, and obtain multiple trolley monitoring data; a parameter determination module, used to construct a single-steering wheel ramp driving dynamics model, synchronize the multiple trolley monitoring data to the single-steering wheel ramp driving dynamics model, and obtain a ramp driving parameter set; and a ramp control module, used to activate an inductive coupling module and a DC voltage control module to collaboratively control the operation of the unmanned trolley according to the ramp driving parameter set, generate a graded braking control strategy, and execute the graded braking control strategy to intelligently control the ramp driving of the unmanned trolley.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: by measuring the slope angle of the vehicle in real time through the inertial measurement unit, synchronizing it to the single steering wheel slope driving dynamics model, determining the slope driving parameter set, activating the inductive coupling module and DC voltage control to generate a graded braking control strategy, improving the slope driving control accuracy, thereby improving the slope driving safety of the driverless trolley and eliminating the risk of slipping and loss of control. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the slope driving control method for the driverless electric vehicle of this application.

[0018] Figure 2 This is a schematic diagram of the system architecture of the single-steering wheel unmanned electric vehicle of this application.

[0019] Figure 3 This is a structural schematic diagram of the single-steering wheel type driverless electric vehicle of this application.

[0020] Figure 4 This is a schematic diagram of the slope driving control system of the driverless electric vehicle of this application.

[0021] Explanation of reference numerals in the attached diagram: Vehicle controller 1, Motor controller 2, Electromagnetic brake 3, Motor 4, DC voltage control module 5, Inductive coupling module 6, Slope monitoring module 11, Parameter determination module 12, Slope control module 13. Detailed Implementation

[0022] This application provides a slope driving control method and system for driverless trolleys, solving the technical problems in existing technologies where driverless trolleys are prone to slipping when stopping and starting on inclines, and prone to speed loss and downward spurts when going downhill. These issues lead to low slope driving control accuracy, further affecting driving safety. By using an inertial measurement unit to measure the slope angle of the vehicle in real time and synchronizing it with a single-steering wheel slope driving dynamics model, the application determines the slope driving parameter set. It then activates the inductive coupling module and DC voltage control to generate a graded braking control strategy, improving slope driving control accuracy and thus enhancing the slope driving safety of the driverless trolley, eliminating the risks of slipping and loss of control.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0024] Example 1, please refer to the appendix. Figure 1 This application provides a method for controlling the ramp travel of an unmanned trolley, wherein the method specifically includes the following steps:

[0025] The inertial measurement unit measures the slope angle of the vehicle in real time, and monitors the driverless trolley based on the slope angle to obtain multiple trolley monitoring data.

[0026] Furthermore, this application also includes the following steps: using an inertial measurement unit to measure the speed of the driverless trolley in real time according to the sampling frequency to obtain a vehicle speed dataset; performing attitude calculation on the driverless trolley according to the vehicle speed dataset to obtain the vehicle's horizontal pitch angle; using the vehicle's horizontal pitch angle as the driving slope angle value of the driverless trolley, and activating the slope driving monitoring mode based on the driving slope angle value; monitoring the driverless trolley based on the slope driving monitoring mode to obtain multiple trolley monitoring data.

[0027] Specifically, an inertial measurement unit (IMU) is a sensor assembly that typically includes a three-axis gyroscope and a three-axis accelerometer. It usually contains an accelerometer, gyroscope, and magnetometer, used to measure the angular velocity and linear acceleration of an object in three-dimensional space. The IMU uses the accelerometer to measure the speed of an autonomous vehicle in real time at a sampling frequency, acquiring the vehicle's acceleration and angular velocity information to obtain a vehicle speed dataset. The sampling frequency is the number of times the IMU collects data per second, usually measured in Hz. For example, 100Hz means 100 data acquisitions per second. The higher the sampling frequency, the higher the real-time performance and accuracy of the data. The vehicle speed dataset is obtained by combining the accelerometer data from the IMU with possible data from wheel speedometers, etc., to determine the speed of the autonomous vehicle at different points in time. It usually includes timestamps and corresponding speed values. For example, an autonomous vehicle with a 2-ton load might be traveling at 8 km / h up a slope with a gradient of 6% (approximately 3.43 degrees), with the IMU sampling the data in real time at a frequency of 100Hz.

[0028] Attitude calculations are performed on the autonomous electric vehicle based on the vehicle speed dataset. The attitude calculation algorithm, such as the Kalman filter, deeply integrates the vehicle speed dataset with the angular velocity signal from the gyroscope and the direct measurements from the accelerometer. A vehicle motion model is constructed, using speed changes to help distinguish and eliminate motion acceleration interference caused by the vehicle's own acceleration or deceleration in the accelerometer signal, thus more accurately separating the perpetually vertically downward gravitational acceleration component. By analyzing the direction of this purified gravitational acceleration in the vehicle coordinate system, the vehicle's horizontal pitch angle can be accurately calculated, revealing the current slope inclination of the vehicle. Attitude calculations, by analyzing sensor data such as vehicle acceleration and angular velocity, deduce the vehicle's spatial attitude, i.e., the position and angle of the vehicle body relative to the ground, including pitch angle, roll angle, and yaw angle. The horizontal pitch angle, also known as the vehicle's roll angle, represents the angle between the vehicle's front and rear axles and the horizontal plane. When the vehicle is going uphill, the pitch angle is positive; when going downhill, the pitch angle is negative. For example, the inertial measurement unit (IMU) collects data at a frequency of 100Hz and uses an adaptive Kalman filter to receive real-time vehicle speed datasets, such as a speed that uniformly increases from 0 to 5 km / h. Combined with gyroscope and accelerometer data, it successfully filters out longitudinal acceleration interference caused by motor drive. Approximately 0.3 seconds after startup, the output vehicle horizontal pitch angle stabilizes between 4.5° and 4.6°, with an error of less than 0.1° compared to the actual slope. In contrast, in a dynamic scenario of emergency braking downhill, traditional calculation methods without speed data assistance exhibit instantaneous jumps of over 2° in pitch angle readings due to the interference of drastic deceleration. This method, however, effectively compensates for motion acceleration, maintaining a stable reading around -3.0°, demonstrating excellent anti-interference capability.

[0029] The vehicle's horizontal pitch angle is used as the slope angle value for the driverless trolley. When the absolute value of the slope angle exceeds 5%, the vehicle is immediately determined to have entered a slope condition, and the slope driving monitoring mode is activated. The slope driving monitoring mode is activated when driving on a slope. This mode initiates additional control strategies, such as precise braking control and power output adjustment. For example, it begins monitoring parameters such as vehicle speed, steering wheel motor speed, and motor current to ensure that the vehicle's driving process meets safety requirements and avoids dangerous situations.

[0030] In slope monitoring mode, the vehicle controller instructs relevant modules to collect and upload vehicle speed, steering wheel motor speed, and motor current at a higher frequency and priority, forming multiple monitoring data for the trolley. Steering wheel motor speed refers to the motor's rotational speed, and motor current is the current intensity during motor operation, reflecting the motor's output power and the vehicle's dynamic state. For example, when the vehicle speed is stable at 7.8 km / h (slightly decreasing due to slope resistance), the motor speed is 350 rpm, and the motor current is 75A. Calculating the slope using high-frequency inertial measurement unit data has a much faster response speed than the traditional method of indirectly judging the slope solely through changes in vehicle speed. By collecting and analyzing trolley speed, motor speed, and current data in real time, the safe and stable operation of the trolley is ensured, preventing problems such as slippage and loss of control, thereby improving the safety and reliability of the driverless trolley on slopes.

[0031] A single-steering wheel ramp driving dynamics model is constructed, and the monitoring data of the multiple trolleys are synchronized to the single-steering wheel ramp driving dynamics model to obtain a ramp driving parameter set.

[0032] Furthermore, this application also includes the following steps: performing multi-parameter fusion dynamics training based on the multiple trolley monitoring data to construct a single-steering wheel ramp driving dynamics model; parsing the multiple trolley monitoring data to obtain vehicle speed data, steering wheel motor speed data, motor current data, and vehicle load mass data; using the ramp angle value and the vehicle load mass data as input parameters, synchronizing them to the single-steering wheel ramp driving dynamics model for updating to obtain initial driving parameters; using the vehicle speed data, steering wheel motor speed data, and motor current data as state feedback parameters, performing closed-loop correction on the initial driving parameters based on the state feedback parameters to generate the ramp driving parameter set.

[0033] Furthermore, this application also includes the following steps: retrieving historical slope driving datasets of the autonomous trolley, wherein the historical slope driving datasets contain vehicle driving data at multiple slopes; performing load center change analysis on the autonomous trolley based on the vehicle driving data at multiple slopes to obtain vehicle mass distribution parameters; estimating friction force on the autonomous trolley at different slopes based on the vehicle driving data at multiple slopes to obtain tire-road contact friction force parameters; performing operational analysis on the motor of the autonomous trolley based on the vehicle driving data at multiple slopes to obtain motor characteristic parameters; performing power generation analysis on the motor of the autonomous trolley based on the vehicle driving data at multiple slopes to obtain motor energy feedback parameters; fusing and training the vehicle mass distribution parameters, the tire-road contact friction force parameters, the motor characteristic parameters, and the motor energy feedback parameters to construct a multi-dimensional coupling parameter set; and performing dynamic training and verification based on the multi-dimensional coupling parameter set to construct the single-steering wheel slope driving dynamic model.

[0034] Specifically, the system retrieves historical slope driving data for autonomous electric vehicles, including driving data on different slopes over a period of time, such as speed, acceleration, power output, and braking performance. For example, an autonomous electric vehicle accumulated over 1,000 kilometers of driving data in a port area, including data on three typical slopes: 5%, 10%, and 15%.

[0035] By analyzing vehicle driving data at different inclines, and utilizing information on changes in vehicle mass and load, the load center of gravity of the autonomous electric vehicle is analyzed. From historical data, segments showing the vehicle accelerating, moving at a constant speed, or decelerating at various inclines are selected. For each data segment, the total driving force or braking force acting on the vehicle is determined. Simultaneously, the inertial measurement unit and wheel speed sensors provide the vehicle's longitudinal acceleration. Based on the fundamental law F=m*a, the total mass m of the system can be deduced given the force F and acceleration a. To obtain the mass distribution, the gravitational component caused by the incline is utilized. When the vehicle is on a slope, gravity is decomposed into a component perpendicular to the slope and a component along the slope. The component along the slope, m*g*sinθ, is superimposed in the vehicle's dynamic equations. By analyzing the incremental change in motor torque required to maintain the same acceleration at different inclines θ, the gravitational component is accurately separated, thereby calculating the total mass. When the vehicle starts or brakes on a slope, the load shifts between the front and rear axles, affecting the adhesion of the drive wheels and the motor current. By analyzing the dynamic characteristics of this load transfer and combining it with vehicle geometric parameters, the algorithm can inversely deduce the longitudinal position and height of the center of gravity, ultimately obtaining complete vehicle mass distribution parameters. Vehicle mass distribution parameters quantify the total mass of the vehicle load (including batteries and cargo) and its center of gravity position, directly affecting the vehicle's rollover risk and the torque required for climbing hills.

[0036] Driving data from multiple inclines is used to estimate friction for the autonomous electric vehicle at different slopes. The vehicle is briefly brought close to its traction limit, either actively or passively, and the friction coefficient is inferred from the data. Historical data is filtered to identify incline driving segments where slight slippage or ABS / TCS intervention may have occurred, monitoring the slip ratio of the drive wheels. The slip ratio is the percentage difference between the wheel's linear velocity and the vehicle's actual speed. When the vehicle's output driving or braking force is very small, the slip ratio is zero; as the force increases, the slip ratio increases; and when the force reaches the maximum value provided by the road surface, the slip ratio increases sharply. During rapid acceleration uphill, the motor torque gradually increases, and the instantaneous increase in drive wheel speed begins to significantly exceed the vehicle speed increase estimated by the IMU (i.e., the point of slippage). At this point, based on the vehicle's total mass, the slope angle, and the current motor torque, the maximum friction coefficient can be calculated using dynamic equations. During rapid braking downhill, the instantaneous locking or slippage of the drive wheels is monitored. At this point, the coefficient of friction for achieving maximum braking force is calculated based on the vehicle deceleration, slope angle, and vehicle mass. By repeating this analysis on multiple slopes with varying degrees of incline, a set of tire-road contact friction parameters under different normal pressures is obtained. The tire-road contact friction parameters refer to the maximum static friction coefficient between the tire and the road surface, determining the limit of the maximum tangential force (driving or braking force) that the road surface can provide to the tire. When the force the vehicle attempts to output exceeds this limit, the wheels will slip or lock up.

[0037] Based on vehicle driving data at multiple gradients, the performance of the motor under different operating conditions is analyzed. By combining the motor's output power, speed, and current data with the vehicle's acceleration data, characteristic parameters of the motor, such as torque, power output, and efficiency, are calculated. Analyzing the motor's operation at different gradients allows us to obtain its behavioral characteristics under various load conditions. Motor characteristic parameters, such as output power, torque, and efficiency, are key performance parameters of the motor under different operating conditions.

[0038] By analyzing the motor's operating status during tram travel on slopes, especially during downhill and braking processes, the energy feedback effect of the motor is estimated. Motor energy feedback parameters characterize the motor's performance in generator mode, such as the maximum regenerative braking torque (Nm), maximum regenerative power (kW), and feedback efficiency (%) at different speeds.

[0039] By fusing vehicle mass distribution parameters, tire-road contact friction parameters, motor characteristic parameters, and motor energy feedback parameters through training, a multi-dimensional set of coupled parameters is constructed. This set reflects the coupling relationships between factors such as the impact of rearward shift of the center of gravity on drive wheel adhesion under heavy loads, the limitation of motor torque output on low-friction surfaces, and the impact of motor feedback power limits on braking performance. Dynamic training and validation are then performed based on this multi-dimensional coupled parameter set. This involves using a subset of historical data to drive the model, comparing its predictions with real data, and iteratively optimizing the model until its prediction error is less than an acceptable range (e.g., <5%). This successfully constructs a high-precision single-steering wheel ramp driving dynamics model.

[0040] A multi-parameter fusion-based single-steering wheel ramp driving dynamics model includes: a method for acquiring model input parameters (ramp angle measurement using an IMU sensor with an accuracy of ±0.5°; motor speed monitoring using an encoder with a resolution of 1024 PPR); a model construction method (converting parameters such as vehicle mass, ramp angle, and friction coefficient into state-space equations); and an online model update mechanism (refreshing parameters every 100ms). A special protection method quantifies the coupling relationship between motor current, speed, and vehicle speed into control parameters, achieving a prediction accuracy of over 95%.

[0041] Furthermore, this application also includes the following steps: inputting the driving slope angle value into the single-steering wheel slope driving dynamics model to calculate the slope and obtain the gravity component parameters of the driverless trolley; inputting the vehicle load mass data into the single-steering wheel slope driving dynamics model to calculate the mass and obtain the vehicle inertia parameters of the driverless trolley; updating the single-steering wheel slope driving dynamics model based on the gravity component parameters and the vehicle inertia parameters to perform force analysis and obtain the force analysis results; and performing driving calculations for the driverless trolley based on the force analysis results to obtain the initial driving parameters.

[0042] Specifically, multiple trolley monitoring data are analyzed to extract key variables such as vehicle speed, steering wheel motor speed, motor current, and vehicle load mass. Vehicle speed data represents the current speed of the vehicle; steering wheel motor speed data represents the rotational speed of the steering wheel motor in the trolley; motor current data represents the operating current of the motor; and vehicle load mass data represents the trolley's mass information, including the total mass of the trolley itself and its cargo.

[0043] The real-time measured slope angle is substituted into the single-steering wheel slope driving dynamics model to calculate the slope. The slope is calculated using the physical formula m*g*sinθ, accurately obtaining the gravity component parameter applied to the vehicle by the current slope. The gravity component parameter is the component of the vehicle's weight along the slope direction; it hinders the vehicle's forward movement when going uphill and propels the vehicle to accelerate when going downhill.

[0044] Real-time vehicle load mass data is input into a single-steering wheel ramp driving dynamics model for mass calculation, determining the vehicle's overall inertial parameters. These parameters are the vehicle's inertial characteristics, typically related to factors such as mass, mass distribution, and motion state. Overall inertial parameters directly affect the vehicle's acceleration and braking performance.

[0045] The single-steering wheel ramp driving dynamics model is updated based on the gravity component parameters and the vehicle's inertial parameters. Its state is updated, and a force analysis is performed to obtain an accurate force analysis result containing all force vectors, determining the theoretical force situation of the vehicle on the ramp. Based on the force analysis results, vehicle driving calculations are performed. By calculating the total resultant force and inertial response experienced by the vehicle on the ramp, the initial driving parameters of the tram under given conditions are obtained. These initial driving parameters include the vehicle's initial speed, acceleration, and power output, representing the initial conditions at which the vehicle begins to move.

[0046] Furthermore, this application also includes the following steps: constructing an error assessment mechanism, the error assessment mechanism including a first error threshold, a second error threshold, and a third error threshold; calculating the error based on the vehicle mass distribution parameters and the vehicle load mass data to obtain a first error value, and comparing the first error value with the first error threshold; when the first error value is greater than the first error threshold, generating a first state feedback parameter to correct the tire-road contact friction parameter, generating a first correction parameter; calculating the error based on the steering wheel motor speed data and the motor characteristic parameters to obtain a second error value, and comparing the second error value with the second error threshold; when the second ..., and comparing the second error value with the first error threshold, generating a first state feedback parameter to correct the tire-road contact friction parameter, generating a first state feedback parameter to correct the tire-road contact friction parameter, generating a first state feedback parameter to correct the tire-road contact friction parameter, generating a first state feedback parameter to correct the tire-road contact friction parameter, generating a first state feedback parameter to correct the tire-road contact friction parameter, generating a first state feedback parameter to correct the tire-road contact friction When the second error threshold is reached, a second state feedback parameter is generated to correct the torque constant of the motor characteristic parameter, generating a second correction parameter; based on the motor current value data and the motor energy feedback parameter, an error calculation is performed to obtain a third error value, which is then compared with the third error threshold; when the third error value is greater than the third error threshold, a third state feedback parameter is generated to correct the motor equivalent resistance parameter of the motor energy feedback parameter, generating a third correction parameter; based on the first correction parameter, the second correction parameter, and the third correction parameter, combined with the vehicle speed data, the driving of the driverless electric vehicle is integrated to generate the slope driving parameter set.

[0047] Specifically, the error assessment mechanism measures the errors of various parameters in the system and corrects them according to their magnitude. This mechanism dynamically monitors the accuracy of parameters when the vehicle is driving on an incline, promptly detects deviations, and takes appropriate corrective measures. The mechanism includes a first error threshold, a second error threshold, and a third error threshold, which are tolerance limits for parameter deviations set for vehicle mass, motor drive characteristics, and motor feedback characteristics, respectively. When the actual calculated error value exceeds this threshold, the corresponding correction action is triggered.

[0048] Based on real-time perceived vehicle load mass data, a first error value is obtained by comparing it with benchmark vehicle mass distribution parameters, and then compared with a first error threshold. When the mass deviation is too large, it is determined that the vehicle load or center of gravity has changed significantly, which will directly affect the tire's adhesion ability. Therefore, a first state feedback parameter is generated to correct the tire-road contact friction parameter, generating a more accurate first correction parameter. Based on real-time steering wheel motor speed data and corresponding motor current data, a second error value is obtained by comparing it with the current that should be at that speed in the motor characteristic parameter spectrum, and then compared with a second error threshold. When the deviation exceeds the limit, it indicates that the motor's torque output capability has changed due to heating or aging. Therefore, a second state feedback parameter is generated to correct the torque constant of the motor characteristic parameter, generating an updated second correction parameter. During braking, based on real-time motor current data, a third error value is obtained by comparing it with the predicted value of the motor energy feedback parameter, and then compared with a third error threshold. When the deviation exceeds the limit, it indicates that the motor's generator internal resistance or circuit efficiency has changed. Therefore, a third state feedback parameter is generated to correct the motor's equivalent resistance of the motor energy feedback parameter, generating an updated third correction parameter. Based on the first correction parameter, the second correction parameter, and the second correction parameter combined with the vehicle speed data, driving integration calculations are performed in the dynamic model to finally generate a set of slope driving parameters that can reflect the latest and most realistic state of the vehicle.

[0049] For example, suppose a tram with a total mass of 4.5 tons enters a downhill section with a 15% gradient after completing a long-distance transport. The model parameters used during initialization are: mass 4.5 tons, friction coefficient μ = 0.7, and motor torque constant Kt = 1.2 Nm / A. Using real-time IMU and motor torque data, the current total mass of the vehicle is instantaneously estimated to be only 4.1 tons (partial unloading of cargo). The first error value is 400 kg, far exceeding the first error threshold of 150 kg, generating the first state feedback parameter, and re-estimating μ based on the new mass of 4.1 t. Under slight braking, the new first correction parameter μ = 0.65 is measured (due to dust on the road surface). When going uphill, at 1500 rpm and a requested torque of 400 Nm, the measured current is 345 A, while the model predicts a current of 333 A (400 Nm / 1.2 Nm / A). The second error value is 12 A (approximately 3.6%), which does not exceed the second error threshold (15 A, approximately 4.5%), therefore no correction is triggered. When descending a slope, the motor generates electricity at 1800 rpm, requesting a regenerative braking torque of 150 Nm. The measured generating current is 125 A, while the model predicts a current of 136 A (based on the original equivalent resistance). The third error value is 11 A (approximately 8.8%), exceeding the third error threshold (10 A, approximately 7.4%). It is determined that the motor's temperature rise has caused a change in the equivalent resistance, generating a third state feedback parameter to correct the equivalent resistance to the new value and generating a third correction parameter. Based on the new mass of 4.1 t, the new friction coefficient μ=0.65, and the updated feedback parameters, combined with the current vehicle speed of 8 km / h, a new set of slope driving parameters is generated. Under the current conditions, the maximum safe regenerative braking torque should be adjusted to 140 Nm. The controller accordingly limits braking commands, avoiding insufficient braking force and overspeed risks caused by over-reliance on the changed feedback capability.

[0050] By employing a multi-threshold error assessment mechanism, the accuracy of the three most variable and critical parameters—tire-road contact friction parameters, motor characteristic parameters, and motor energy feedback parameters—is ensured. This ensures that the final set of slope driving parameters always remains consistent with the vehicle's actual state, fundamentally solving the long-standing problem of decreased control performance or even failure due to parameter mismatch. This guarantees the continuous safety, stability, and energy efficiency of heavy-duty autonomous electric vehicles throughout their entire lifecycle and under various complex operating conditions.

[0051] According to the slope driving parameter set, the inductive coupling module and the DC voltage control module are activated to coordinate the operation of the driverless trolley, generate a graded braking control strategy, and execute the graded braking control strategy to intelligently control the slope driving of the driverless trolley.

[0052] Furthermore, this application also includes the following steps: identifying operating conditions based on the slope driving parameter set to determine multiple slope operating condition types; activating the inductive coupling module and the DC voltage control module to perform braking classification on the driverless trolley according to the multiple slope operating condition types, and defining multiple braking levels; performing operation control analysis on the driverless trolley according to the multiple braking levels to construct a graded braking control strategy; and executing the graded braking control strategy based on the inductive coupling module and the DC voltage control module.

[0053] Furthermore, this application also includes the following steps: analyzing the multiple slope operating conditions to obtain uphill and downhill operating conditions; when the slope operating condition type of the driverless trolley is the uphill operating condition, activating the inductive coupling module to perform instantaneous torque compensation to obtain the anti-rollover braking level and normal driving level; when the slope operating condition type of the driverless trolley is the downhill operating condition, activating the DC voltage control module to perform braking classification to obtain the light braking level, moderate braking level, and emergency braking level; performing cooperative driving analysis on the driverless trolley based on the inductive coupling module and the DC voltage control module to generate cooperative driving analysis results; performing braking analysis on the anti-rollover braking level, normal driving level, light braking level, moderate braking level, and emergency braking level according to the cooperative driving analysis results, and setting an execution priority sequence according to the braking analysis results; sorting the anti-rollover braking level, normal driving level, light braking level, moderate braking level, and emergency braking level according to the execution priority sequence to define the multiple braking levels.

[0054] Furthermore, this application also includes the following steps: controlling the power switch of the inductive coupling module to alternately close and open based on the anti-runaway braking level to obtain anti-runaway torque control parameters; adjusting the discharge current parameter of the DC voltage control module based on the mild braking level to obtain a first braking control parameter; coordinating the operating frequency parameter of the inductive coupling module and the discharge current parameter of the DC voltage control module based on the moderate braking level to obtain a second braking control parameter; simultaneously activating the maximum coupling degree of the inductive coupling module and the discharge current of the DC voltage control module based on the emergency braking level to obtain a third braking control parameter; and adding the first braking control parameter, the second braking control parameter, and the third braking control parameter to the graded braking control strategy.

[0055] Specifically, as shown in the appendix Figure 2 Appendix Figure 3As shown, taking a single-steering wheel type unmanned electric vehicle with a weight of ≥2 tons as an example, its system architecture consists of a vehicle controller 1, a motor controller 2, an electromagnetic brake 3, a motor 4, a DC voltage control module 5, and an inductive coupling module 6. Among them, the vehicle controller 1 (VCU) communicates with the motor controller 2 (MCU) via a CAN bus (250kbps); the motor controller 2 (MCU) controls the torque / speed of the motor 4, monitors the status of the motor 4 and feeds it back to the vehicle controller 1 (VCU); the electromagnetic brake 3 performs braking operations according to the instructions of the motor controller 2 (MCU); the DC voltage control module 5 monitors the bus voltage, and the discharge circuit consumes energy when the voltage is too high; the inductive coupling module 6 works in conjunction with the motor 4, switching the stable current through a power switch.

[0056] The vehicle controller 1 (VCU) is the main control unit, responsible for overall coordination and decision-making; the motor controller 2 (MCU) is used to drive the actuators and realize closed-loop torque / speed control; the electromagnetic brake 3 is the parking safety unit, providing friction braking; the single steering wheel system integrates the drive motor, electromagnetic brake 3, reduction mechanism, and wheel body. The DC voltage control module 5 integrates sampling processing and discharge circuits to dynamically adjust the DC bus voltage. The inductive coupling module 6 consists of a three-phase inductor (typical value 5-10mH) and a power switch (200V 200A), working in conjunction with the motor 4.

[0057] The inductive coupling module 6 uses an 8mm aluminum substrate for reinforced air cooling. The power switch closes when the motor outputs PWM pulses and connects to the motor's four coils, employing ZVSCS soft-switching technology to stabilize the current; it disconnects during normal output. The DC voltage control module 5's hardware design includes a DSP digital signal processor (sampling frequency ≥10kHz), a differential voltage acquisition circuit (accuracy ±0.5%), and a three-stage bleeder circuit (MOSFET + power resistor combination, resistance 0.5Ω ±5%). Its workflow includes real-time monitoring of the DC bus voltage (sampling period 100μs), dynamic comparison of safety thresholds (adjustable from 24-36V), graded bleeder control (20A for Vbus≥28V, 50A for ≥32V, 90A for ≥35V), and fault protection (sending a fault code when continuous overvoltage >10s).

[0058] For downhill braking control, when the motor controller 2 detects that the speed exceeds the threshold (0.5-2 km / h) or the gradient exceeds the preset value, it activates the pulse width modulation (PWM) pulse output to activate the discharge circuit to consume excess energy and prevent battery overcharging. The vehicle controller 1 monitors the speed and controls the electromagnetic brake 3 (duty cycle adjustable from 10% to 90%) via the PWM. The faster the vehicle accelerates, the higher the braking frequency (negative feedback control).

[0059] For uphill start control, the interlocking release function prioritizes motor torque output; the preload torque is calculated based on the formula m*g*sinθ (including ±10% safety margin). Motor 4 first outputs reverse torque to maintain balance, then releases electromagnetic brake 3, with a torque ramp control slope of 50 Nm / s. For parking control, upon receiving a parking command, motor controller 2 reduces its output. When the speed drops to a critical value, it outputs reverse torque to bring the vehicle to a complete stop. For hill start anti-rollover, when starting downhill, motor controller 2 first outputs reverse torque. After the vehicle is balanced, electromagnetic brake 3 is activated, and a pulse output descends. Once the speed stabilizes, the vehicle slows downhill.

[0060] The slope driving monitoring process is as follows: Vehicle starts, and vehicle controller 1 initializes; the inertial measurement unit measures the slope angle. Vehicle speed is monitored in real time. Driving direction is determined: Uphill: preload torque calculation is performed, motor reverse torque output is activated, and speed is monitored to prevent slippage; Downhill: PWM pulse output is activated, the discharge circuit is activated, and speed is monitored to prevent loss of control. Upon receiving a stop command, motor 4 reverse output and electromagnetic brake 3 activation sequence are executed. The process ends when the vehicle comes to a complete stop.

[0061] An inductive coupling module 6 was added externally to motor 4, and a DC voltage control module 5 was added to the DC input terminal of motor controller 2. The inductive coupling module 6 consists of a three-phase inductor and a power switch. The hardware architecture of the DC voltage control module 5 is as follows: The DC voltage control module 5 consists of a DC voltage acquisition and comparison circuit and a bleeder circuit, including a DSP digital signal processor (sampling frequency ≥10kHz), a differential voltage acquisition circuit (accuracy ±0.5%), a three-stage bleeder circuit (MOSFET + power resistor combination), and a relay redundancy protection unit. Interface definition: B+ connects to the positive terminal of the DC bus; B- connects to the negative terminal of the DC bus; M1 / M2 connects to the bleeder resistor (resistance value 0.5Ω ±5%). Workflow: Real-time monitoring stage: DSP continuously samples the DC bus voltage (sampling period 100μs); Threshold judgment stage: Dynamically compares the voltage value with the safety threshold (threshold range 24-36V adjustable); Graded discharge control: When Vbus≥28V, a 20A discharge current is activated; when Vbus≥32V, a 50A discharge current is activated; when Vbus≥35V, a 90A discharge current is activated; Fault protection: When continuous overvoltage (>10s) is detected, a fault code is sent to the vehicle controller 1 via the CAN bus. Control method: Downhill braking: Linked with inductive coupling module 6, the discharge circuit is activated when the PWM pulse is output; Uphill start: The discharge function is locked, prioritizing motor torque output. Communication protocol: CAN2.0B protocol is adopted.

[0062] Vehicle controller 1 monitors the vehicle's transition to downhill driving, and motor controller 2 outputs deceleration, with a speed range of 0.5-2 km / h. Motor controller 2 monitors the speed of motor 4; when motor 4 accelerates, motor controller 2 switches from constant output to PWM pulse output. Vehicle controller 1 monitors vehicle speed; when vehicle speed increases, motor controller 2 intermittently activates electromagnetic brake 3, with shorter intervals for activation as the vehicle accelerates. Upon receiving a stop command, vehicle controller 1 first reduces the output of motor controller 2, causing motor 4 to slow down and control vehicle speed reduction. When the vehicle speed reaches a critical value, motor controller 2 reverses its output to bring the vehicle to a complete stop. For downhill start control, motor controller 2 first outputs reverse torque; after the vehicle balances, motor controller 2 then activates electromagnetic brake 3, and the vehicle pulses downwards. Once the speed stabilizes, it transitions to slow downhill driving. During motor controller 2's pulse output, the power switch of inductive coupling module 6 closes, connecting inductive coupling module 6 to the motor coil and aiding in stable motor 4 operation. When the motor controller 2 is normally outputting to control the motor 4, the power switch of the inductive coupling module 6 is turned off. The inductive coupling module 6 adopts ZVSCS soft switching technology.

[0063] The physical structure design of the inductive coupling module 6 and the motor co-control system includes: inductor parameter selection (5-10mH), power switch selection (200V 200A), connection method with motor 4 (closed during PWM, open during normal operation), and ZVSCS soft-switching technology. Functions include suppressing current surges and improving system response speed by ≥30%. An inductive coupling module 6 is added externally to motor 4. This module consists of a three-phase inductor and a power switch, working in conjunction with the motor controller 2. Specific protection measures include: the physical structure design of the inductive coupling module 6, including inductor parameter selection (typically 5-10mH) and power switch selection (200V 200A); the connection method between the module and motor 4, especially the switching logic of the power switch under different operating states of motor 4 (e.g., closed during PWM pulse output, open during normal output); and the specific implementation method of the ZVSCS soft-switching technology used in the module. This solves the problem of unstable motor 4 control in traditional systems when driving on slopes. Inductive coupling effectively suppresses current surges and improves system response speed by more than 30%.

[0064] The hardware design of the dynamic adjustment mechanism of the DC voltage control module 5 includes a voltage acquisition and comparison circuit (differential amplifier + comparator), a bleeder circuit topology (MOSFET + power resistor), and interface definitions (B+ / B- / M1 / M2). It adjusts the voltage threshold in real time based on the slope (accuracy ±0.5V) and provides a fast response to voltage surges (<10ms). The hardware design and operating principle of the DC voltage control module 5 are protected, including the specific implementation scheme of the voltage acquisition and comparison circuit (e.g., using a differential amplifier circuit + comparator architecture); the topology of the bleeder circuit (MOSFET switch + power resistor combination); and the interface definition and data interaction protocol between the module and the motor controller 2. The dynamic adjustment algorithm is particularly protected, including an adaptive strategy for real-time voltage threshold adjustment based on the slope angle (adjustment accuracy up to ±0.5V) and a fast response mechanism (response time <10ms) when a voltage surge is detected. This effectively prevents excessive DC bus voltage caused by energy feedback during downhill driving.

[0065] The system employs a graded braking control method for different gradient conditions, including speed range division standards: 0-2 km / h, 2-5 km / h, or >5 km / h; corresponding braking mode combinations for each range (PWM pulse + reverse braking + electromagnetic braking); and dynamic adjustment algorithms for braking parameters (such as the functional relationship between braking frequency and gradient and load). It also incorporates a negative feedback control logic that activates the electromagnetic brake with shorter gaps as the vehicle accelerates, ensuring downhill speed fluctuations are controlled within ±0.3 km / h. Furthermore, it includes optimized management methods for regenerative braking to prevent battery overcharging.

[0066] The anti-slip-off control sequence for uphill starts includes a pre-tensioning torque calculation model (based on the mgsinθ formula, considering a ±10% safety margin); the output timing of the motor's reverse torque (first outputting reverse torque to maintain balance, then releasing the electromagnetic brake 3); and a torque ramp control curve (adjustable slope, typical value 50 Nm / s). The system prioritizes protecting the vehicle when its speed drops to a critical value. When this occurs, the motor controller 2 outputs reverse closed-loop control logic to keep the slip-off distance within 0.2 meters. It also includes an adaptive parameter adjustment algorithm for different slope conditions.

[0067] The intelligent control strategy for the electromagnetic brake 3 under slope conditions includes a braking torque calculation model (considering the temperature decay coefficient); operating mode switching logic (conversion conditions between intermittent braking and continuous braking); and thermal management methods (derating curves based on temperature prediction). It emphasizes the protection of PWM control (duty cycle 10%-90%) pulse control method, achieving precise braking force control by adjusting the duty cycle (10%-90% adjustable), keeping the brake pad temperature below 150°C. It also includes brake status monitoring and fault self-diagnosis methods.

[0068] The vehicle control system features a multi-level safety protection architecture, including fault detection mechanisms (setting abnormal thresholds for parameters such as voltage, current, and temperature); fault handling strategies (graded derating, emergency braking, etc.); and system redundancy design (dual-channel acquisition of critical signals). It specifically protects the motor controller 2 by providing a reverse safety stop control sequence, including a dynamic calculation method for speed thresholds and a reverse torque output curve design, enabling emergency stopping within 300ms, 50% faster than traditional systems. It also includes a system status monitoring interface and alarm log recording methods.

[0069] The integrated design methodology for each hardware module includes the definition of the communication interface between the vehicle controller 1 and the motor controller 2 (CAN bus protocol, 250kbps); the selection of the installation location for the DC voltage control module 5 (close to the DC input terminal of the motor controller 2); and the heat dissipation design for the inductive coupling module 6 (aluminum substrate + forced air cooling). Key protection measures include the signal interaction timing and electrical isolation requirements between modules to ensure system reliability under harsh operating conditions, as well as the vibration resistance performance indicators of each module.

[0070] Specifically, the system identifies operating conditions based on a set of slope driving parameters, determining multiple slope operating condition types, including uphill, downhill, and hill-start assist (SLA) conditions. By analyzing parameters such as vehicle speed, slope angle, and motor torque, the current operating condition type is determined. Analysis is then performed based on these multiple slope operating condition types to clearly distinguish the specific scenarios of uphill and downhill conditions. In other words, the current driving state of the trolley is analyzed according to different operating condition types (uphill, downhill, or SLA). When going uphill, the focus is on preventing rollback; when going downhill, the focus is on braking grading to ensure controllable and safe vehicle speed. When an uphill condition is identified, the inductive coupling module is immediately activated for instantaneous torque compensation, providing additional torque support by rapidly adjusting the inductor current, obtaining the corresponding anti-rollback braking level and normal driving level based on the slope and load conditions. When a downhill condition is identified, the DC voltage control module is activated for braking grading, obtaining a complete braking spectrum from light braking level to emergency braking level by monitoring the DC bus voltage and vehicle acceleration. The anti-rollback braking level is a classification of braking intensity for uphill starting and stopping scenarios, primarily addressing severe rollback issues of 0.5-1 meter. The normal driving level refers to the power output level during normal vehicle operation, without involving braking or acceleration compensation. Different braking levels are defined based on the vehicle's downhill condition, such as light braking, moderate braking, and emergency braking, to address driving risks on different slopes. The light braking level is suitable for downhill conditions with gentle slopes and low speeds; the moderate braking level is suitable for downhill conditions with steep slopes or high speeds; the emergency braking level is typically the strongest braking level used when the slope is very steep or the vehicle is close to losing control.

[0071] The cooperative driving analysis of the autonomous electric vehicle was conducted using an inductively coupled module and a DC voltage control module. The analysis results comprehensively considered the instantaneous torque compensation capability of the inductively coupled module, the discharge capacity and response characteristics of the DC voltage control module, and the energy interaction between the two. A multi-objective optimization algorithm was used to generate cooperative driving analysis results containing optimal control parameters. The cooperative driving analysis results are a dataset containing optimal control strategy recommendations, such as recommended dominant control modules, energy allocation schemes, and safety margin assessments.

[0072] Based on the results of the cooperative driving analysis, braking analysis was conducted on the anti-rollover braking level, normal driving level, light braking level, moderate braking level, and emergency braking level. The analysis of each braking level under the current operating conditions was thoroughly evaluated. An execution priority sequence was established based on the braking analysis results, with the principle of prioritizing safety braking over comfort braking, and active preventative braking over passive response braking. The braking levels were sorted according to the execution priority sequence, clearly defining the triggering conditions and execution order of multiple braking levels, forming a complete control logic system. Anti-rollover braking, normal driving, light braking, moderate braking, and emergency braking were systematically arranged according to priority, defining multiple braking levels to ensure that the most urgent braking needs are met first.

[0073] Through precise operating condition identification and braking grading, the system provides the most suitable control strategy for different slope scenarios: effectively solving the severe rollover problem of 0.5-1 meter when going uphill, controlling the rollover distance within 0.2 meters; and controlling speed fluctuations within ±0.3 km / h through multi-level braking coordination when going downhill. The coordinated operation of the inductive coupling module and the DC voltage control module ensures smooth transition and optimal control under various operating conditions, significantly improving the driving safety, stability, and energy utilization efficiency of the heavy-duty driverless trolleybus under ≥15% slope conditions.

[0074] The operation control of the driverless trolley was analyzed based on multiple braking levels. Taking into account the characteristics and execution conditions of each braking level, a graded braking control strategy was constructed. That is, different intensity and type of braking control strategies are adopted according to different driving conditions and urgency levels. Each braking level (anti-rollback braking, normal driving, light braking, moderate braking, and emergency braking) corresponds to different control methods and parameters to ensure safe vehicle operation.

[0075] To meet the rapid response requirements of the anti-rollback braking level, the power switch of the inductive coupling module is alternately closed and opened. By adjusting the switch duty cycle and frequency, the compensation torque is precisely controlled, thereby obtaining accurate anti-rollback torque control parameters. The power switch of the inductive coupling module closes when the motor controller outputs PWM pulses and opens when the motor is operating normally. The anti-rollback torque control parameters are motor torque-related parameters used to prevent the vehicle from rolling backward, generated by controlling the alternating on and off of the inductive coupling module power switch under the uphill anti-rollback braking level.

[0076] For mild braking levels with small braking requirements, the DC voltage control module limits the discharge current parameter, and appropriate braking force is provided by controlling the conduction degree of the discharge circuit, thus obtaining the corresponding first braking control parameters. The first braking control parameters are the set of control parameters that achieve the braking effect mainly by adjusting the discharge current at the mild braking level. For moderate braking levels with stronger braking requirements, based on the balance requirements of moderate braking levels, the operating frequency parameter of the inductive coupling module and the discharge current parameter of the DC voltage control module are coordinated to achieve synergistic operation of electric braking and energy discharge, resulting in optimized second braking control parameters. The second braking control parameters are composite control parameters formed by coordinating the operating frequency and discharge current parameters of the inductive coupling module at the moderate braking level. At the most extreme emergency braking level, the maximum coupling degree of the inductive coupling module and the discharge current of the DC voltage control module are activated simultaneously to fully utilize the maximum braking capacity of both modules, resulting in the strongest third braking control parameters. The third braking control parameters are the limit control parameters that maximize the braking capacity of both inductive coupling and the discharge circuit at the emergency braking level. Maximum coupling is the maximum output torque capability of the inductive coupling module, used to provide forced traction or braking force. Maximum discharge current is the maximum feedback current of the DC voltage control module, which can provide a strong braking effect.

[0077] For example, a graded braking control verification was conducted on an unmanned charging vehicle with a load of 2.5 tons on a comprehensive test slope with a gradient of 20% and a length of 30 meters. The vehicle started on the 20% slope, activating the anti-roll-off braking level. The power switch of the inductive coupling module alternately closed and opened at a frequency of 5kHz and a duty cycle of 70%. The measured anti-roll-off torque control parameters were a compensation torque of 185Nm and a response time of 80ms. Actual testing showed that after applying these parameters, the roll-off distance decreased from 0.8 meters to 0.16 meters. When the downhill speed deviation reached 1.2km / h, a light braking level was triggered. The DC voltage control module limited the discharge current parameter to 25A, and the first braking control parameters included a discharge power of 6.5kW and a braking deceleration of 0.3m / s². The measured speed recovery time to the target value was 4.2 seconds, and the energy recovery efficiency was 87%. When the speed deviation is 2.8 km / h, a medium braking level is activated. The operating frequency parameter of the inductive coupling module (3.8 kHz) and the discharge current parameter (55 A) are coordinated. The second braking control parameters achieve a total braking torque of 210 Nm and a discharge power of 15.2 kW. During braking, the DC bus voltage remains stable below 33 V, with a speed control accuracy of ±0.25 km / h. A sudden obstacle is simulated, triggering an emergency braking level. Simultaneously, the maximum coupling of the inductive coupling module is activated, with a switching frequency of 8 kHz and a duty cycle of 95%; the discharge current of the DC voltage control module reaches its maximum value of 90 A. The third braking control parameters provide a peak braking torque of 385 Nm and an instantaneous discharge power of 25 kW. Test results show that the distance from 10 km / h to a complete stop during emergency braking is 2.8 meters, and the braking time is 2.1 seconds.

[0078] By adding control parameters at various levels to the graded braking control strategy, a complete parameterized and executable control scheme is formed. The graded braking control strategy enables intelligent control of the driverless trolley's slope driving, achieving anti-rollback control on uphill sections and speed stability control on downhill sections. It switches between uphill and downhill conditions and performs intelligent control based on real-time monitoring data (such as vehicle speed, slope angle, and motor speed). Through real-time analysis of operating conditions, it automatically selects the appropriate anti-rollback control or speed stability control mode to ensure the trolley is always in optimal driving condition. By implementing the graded braking control strategy, the driverless trolley can intelligently adjust its driving state in complex slope environments, preventing rollback on uphill sections and ensuring speed stability on downhill sections. Precise control of motor torque, discharge current, and braking level achieves safe driving control under different slope conditions, greatly improving the reliability and safety of the trolley when driving on slopes.

[0079] In summary, the slope driving control method for unmanned electric vehicles provided in this application has the following technical effects: by measuring the slope angle of the vehicle in real time through the inertial measurement unit, synchronizing it to the single steering wheel slope driving dynamics model, determining the slope driving parameter set, activating the inductive coupling module and DC voltage control to generate a graded braking control strategy, the slope driving control accuracy is improved, thereby enhancing the slope driving safety of the unmanned electric vehicle and eliminating the risk of slipping and loss of control.

[0080] Example 2: Based on the same inventive concept as the ramp driving control method for the driverless trolley in Example 1, this application also provides a ramp driving control system for driverless trolleys. Please refer to the appendix. Figure 4 The system includes: a slope monitoring module 11, used to measure the slope angle of the vehicle in real time through an inertial measurement unit, monitor the driverless trolley based on the slope angle, and obtain multiple trolley monitoring data; a parameter determination module 12, used to construct a single-steering wheel slope driving dynamics model, synchronize the multiple trolley monitoring data to the single-steering wheel slope driving dynamics model, and obtain a slope driving parameter set; and a slope control module 13, used to activate the inductive coupling module and the DC voltage control module to coordinate the operation of the driverless trolley according to the slope driving parameter set, generate a graded braking control strategy, and execute the graded braking control strategy to intelligently control the slope driving of the driverless trolley.

[0081] Furthermore, the slope monitoring module 11 in the slope driving control system of the driverless trolley is also used to: measure the speed of the driverless trolley in real time according to the sampling frequency through the inertial measurement unit to obtain a vehicle speed dataset; perform attitude calculation on the driverless trolley according to the vehicle speed dataset to obtain the vehicle horizontal pitch angle; use the vehicle horizontal pitch angle as the driving slope angle value of the driverless trolley, and activate the slope driving monitoring mode based on the driving slope angle value; monitor the driverless trolley based on the slope driving monitoring mode to obtain multiple trolley monitoring data.

[0082] Furthermore, the parameter determination module 12 in the ramp driving control system of the driverless trolley is also used for: performing multi-parameter fusion dynamic training based on the multiple trolley monitoring data to construct a single steering wheel ramp driving dynamic model; parsing the multiple trolley monitoring data to obtain vehicle speed data, steering wheel motor speed data, motor current value data, and vehicle load mass data; using the ramp angle value and the vehicle load mass data as input parameters, synchronizing them to the single steering wheel ramp driving dynamic model for updating to obtain initial driving parameters; using the vehicle speed data, steering wheel motor speed data, and motor current value data as state feedback parameters, performing closed-loop correction on the initial driving parameters based on the state feedback parameters to generate the ramp driving parameter set.

[0083] Furthermore, the parameter determination module 12 in the ramp driving control system of the driverless trolley is also used for: retrieving the historical ramp driving dataset of the driverless trolley, which includes vehicle driving data at multiple slopes; performing load center change analysis on the driverless trolley based on the vehicle driving data at multiple slopes to obtain vehicle mass distribution parameters; estimating the friction force of the driverless trolley at different slopes based on the vehicle driving data at multiple slopes to obtain tire-road contact friction force parameters; performing operation analysis on the motor of the driverless trolley based on the vehicle driving data at multiple slopes to obtain motor characteristic parameters; performing power generation analysis on the motor of the driverless trolley based on the vehicle driving data at multiple slopes to obtain motor energy feedback parameters; fusing and training the vehicle mass distribution parameters, the tire-road contact friction force parameters, the motor characteristic parameters, and the motor energy feedback parameters to construct a multi-dimensional coupled parameter set; and performing dynamic training and verification based on the multi-dimensional coupled parameter set to construct the single-steering wheel ramp driving dynamic model.

[0084] Furthermore, the parameter determination module 12 in the ramp driving control system of the driverless trolley is also used for: inputting the ramp angle value into the single steering wheel ramp driving dynamics model to calculate the ramp angle and obtain the gravity component parameters of the driverless trolley; inputting the vehicle load mass data into the single steering wheel ramp driving dynamics model to calculate the mass and obtain the vehicle inertia parameters of the driverless trolley; updating the single steering wheel ramp driving dynamics model based on the gravity component parameters and the vehicle inertia parameters to perform force analysis and obtain the force analysis results; and performing driving calculations of the driverless trolley based on the force analysis results to obtain the initial driving parameters.

[0085] Furthermore, the parameter determination module 12 in the ramp driving control system of the driverless electric vehicle is also used for: constructing an error evaluation mechanism, the error evaluation mechanism including a first error threshold, a second error threshold, and a third error threshold; calculating the error based on the vehicle mass distribution parameters and the vehicle load mass data to obtain a first error value, and comparing the first error value with the first error threshold; when the first error value is greater than the first error threshold, generating a first state feedback parameter to correct the tire-road contact friction parameter, and generating a first correction parameter; calculating the error based on the steering wheel motor speed data and the motor characteristic parameters to obtain a second error value, and comparing the second error value with the second error threshold. When the second error value is greater than the second error threshold, a second state feedback parameter is generated to correct the torque constant of the motor characteristic parameter, generating a second correction parameter; based on the motor current value data and the motor energy feedback parameter, an error calculation is performed to obtain a third error value, which is compared with the third error threshold; when the third error value is greater than the third error threshold, a third state feedback parameter is generated to correct the motor equivalent resistance parameter of the motor energy feedback parameter, generating a third correction parameter; based on the first correction parameter, the second correction parameter, and the third correction parameter, combined with the vehicle driving speed data, the driving of the driverless electric vehicle is integrated to generate the slope driving parameter set.

[0086] Furthermore, the ramp control module 13 in the ramp driving control system of the driverless trolley is also used for: identifying operating conditions based on the ramp driving parameter set to determine multiple ramp operating condition types; activating the inductive coupling module and the DC voltage control module to perform braking classification on the driverless trolley according to the multiple ramp operating condition types, and defining multiple braking levels; performing operation control analysis on the driverless trolley according to the multiple braking levels to construct a graded braking control strategy; and executing the graded braking control strategy based on the inductive coupling module and the DC voltage control module.

[0087] Furthermore, the slope control module 13 in the slope driving control system of the driverless trolley is also used for: analyzing the multiple slope operating conditions to obtain uphill and downhill operating conditions; when the slope operating condition of the driverless trolley is the uphill condition, activating the inductive coupling module to perform instantaneous torque compensation to obtain anti-rollover braking level and normal driving level; when the slope operating condition of the driverless trolley is the downhill condition, activating the DC voltage control module to perform braking classification to obtain light braking level, moderate braking level, and emergency braking level; based on the inductive coupling... The combined module and the DC voltage control module perform cooperative driving analysis on the driverless electric vehicle and generate cooperative driving analysis results; based on the cooperative driving analysis results, perform braking analysis on the anti-rollover braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level, and set an execution priority sequence based on the braking analysis results; sort the anti-rollover braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level according to the execution priority sequence, and define the multiple braking levels.

[0088] Furthermore, the ramp control module 13 in the ramp driving control system of the driverless electric vehicle is also used to: control the power switch of the inductive coupling module to alternately close and open based on the anti-runaway braking level to obtain anti-runaway torque control parameters; adjust the discharge current parameter of the DC voltage control module based on the light braking level to obtain a first braking control parameter; coordinate the operating frequency parameter of the inductive coupling module and the discharge current parameter of the DC voltage control module based on the medium braking level to obtain a second braking control parameter; simultaneously activate the maximum coupling degree of the inductive coupling module and the discharge current of the DC voltage control module based on the emergency braking level to obtain a third braking control parameter; and add the first braking control parameter, the second braking control parameter, and the third braking control parameter to the graded braking control strategy.

[0089] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The slope driving control method and specific examples of the driverless trolley in Example 1 are also applicable to the slope driving control system of the driverless trolley in this example.

[0090] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0091] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for controlling ramp travel of an unmanned electric vehicle, characterized in that, include: The inertial measurement unit measures the slope angle of the vehicle in real time, and monitors the driverless trolley based on the slope angle to obtain multiple trolley monitoring data. A single steering wheel slope driving dynamics model is constructed, and the monitoring data of the multiple trams are synchronized to the single steering wheel slope driving dynamics model to obtain a slope driving parameter set. Based on the slope driving parameter set, the inductive coupling module and the DC voltage control module are activated to collaboratively control the operation of the driverless trolley, generating a graded braking control strategy. This graded braking control strategy is then executed to intelligently control the driverless trolley's slope driving, including: Based on the set of ramp driving parameters, operating conditions are identified to determine multiple ramp operating condition types. Based on the multiple ramp conditions, the inductive coupling module and the DC voltage control module are activated to perform braking classification on the driverless trolley, defining multiple braking levels, including: Based on the analysis of the multiple slope working conditions, uphill working conditions and downhill working conditions are obtained; When the slope condition of the driverless electric vehicle is the uphill condition, the inductive coupling module is activated to perform instantaneous torque compensation to obtain the anti-slip braking level and the normal driving level. When the slope condition of the driverless electric vehicle is the downhill condition, the DC voltage control module is activated to perform braking classification, obtaining light braking level, medium braking level, and emergency braking level. Based on the inductive coupling module and the DC voltage control module, a cooperative driving analysis of the driverless electric vehicle is performed, and a cooperative driving analysis result is generated. Based on the cooperative driving analysis results, braking analysis is performed on the anti-slip braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level, and an execution priority sequence is set based on the braking analysis results; The anti-slip braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level are sorted according to the execution priority sequence to define the multiple braking levels; Based on the multiple braking levels, the operation control analysis of the driverless electric vehicle is carried out to construct a graded braking control strategy; The graded braking control strategy is executed based on the inductive coupling module and the DC voltage control module. An inductive coupling module is added to the outside of the motor, and a DC voltage control module is added to the DC input terminal of the motor controller. The inductive coupling module works in conjunction with the motor to switch the stable current through a power switch. The DC voltage control module integrates sampling processing and discharge circuits to dynamically adjust the DC bus voltage.

2. The slope driving control method for an unmanned trolley as described in claim 1, characterized in that, The inertial measurement unit measures the slope angle of the vehicle in real time, and monitors the driverless trolley based on the slope angle to obtain multiple trolley monitoring data, including: The inertial measurement unit is used to measure the speed of the driverless electric vehicle in real time according to the sampling frequency to obtain the vehicle speed dataset; The attitude of the driverless trolley is calculated based on the vehicle speed dataset to obtain the vehicle's horizontal pitch angle. The vehicle's horizontal pitch angle is used as the driving slope angle value of the driverless electric vehicle, and the slope driving monitoring mode is activated based on the driving slope angle value. The driverless trolley is monitored based on the slope driving monitoring mode to obtain multiple trolley monitoring data.

3. The slope driving control method for an unmanned trolley as described in claim 2, characterized in that, A single-steering wheel ramp driving dynamics model is constructed, and the monitoring data of the multiple trolleys are synchronized to the single-steering wheel ramp driving dynamics model to obtain a ramp driving parameter set, including: Based on the multiple trolley monitoring data, multi-parameter fusion dynamics training was performed to construct a single steering wheel slope driving dynamics model. The multiple trolley monitoring data are analyzed to obtain vehicle speed data, steering wheel motor speed data, motor current value data, and vehicle load mass data; The driving slope angle value and the vehicle load mass data are used as input parameters and synchronized to the single steering wheel slope driving dynamics model for updating to obtain initial driving parameters. The vehicle speed data, the steering wheel motor speed data, and the motor current value data are used as state feedback parameters. Based on the state feedback parameters, the initial driving parameters are corrected in a closed loop to generate the slope driving parameter set.

4. The slope driving control method for an unmanned trolley as described in claim 3, characterized in that, Based on the aforementioned multiple trolley monitoring data, multi-parameter fusion dynamics training is performed to construct a single-steering wheel slope driving dynamics model, including: Retrieve the historical slope driving dataset of the driverless electric vehicle, which contains vehicle driving data for multiple slopes; Based on vehicle driving data from multiple slopes, load center change analysis was performed on the driverless trolley to obtain vehicle mass distribution parameters. Based on vehicle driving data at multiple slopes, friction force is estimated for driverless electric vehicles at different slopes to obtain tire-road contact friction force parameters. The operation of the motor of the driverless trolley is analyzed based on vehicle driving data at multiple slopes to obtain motor characteristic parameters. Based on vehicle driving data from multiple slopes, the power generation of the motor in the driverless trolley is analyzed to obtain motor energy feedback parameters. The vehicle mass distribution parameters, tire-road contact friction parameters, motor characteristic parameters, and motor energy feedback parameters are fused and trained to construct a multi-dimensional coupled parameter set; Based on the multi-dimensional coupling parameter set, dynamic training and verification are performed to construct the single-steering wheel ramp driving dynamic model.

5. The slope driving control method for an unmanned trolley as described in claim 3, characterized in that, The driving slope angle value and the vehicle load mass data are used as input parameters and synchronized to the single-steering wheel slope driving dynamics model for updating to obtain initial driving parameters, including: The driving slope angle value is input into the single steering wheel slope driving dynamics model to calculate the slope and obtain the gravity component parameters of the driverless electric vehicle. The vehicle load mass data is input into the single steering wheel ramp driving dynamics model for mass calculation to obtain the whole vehicle inertial parameters of the driverless electric vehicle. Based on the gravity component parameters and the vehicle inertia parameters, the single steering wheel slope driving dynamics model is updated for force analysis to obtain the force analysis results; Based on the force analysis results, the driving calculation of the driverless electric vehicle is performed to obtain the initial driving parameters.

6. The slope driving control method for an unmanned trolley as described in claim 4, characterized in that, Using the vehicle speed data, the steering wheel motor speed data, and the motor current data as state feedback parameters, the initial driving parameters are corrected using closed-loop correction based on the state feedback parameters to generate the slope driving parameter set, including: An error assessment mechanism is constructed, which includes a first error threshold, a second error threshold, and a third error threshold. Based on the vehicle mass distribution parameters and the vehicle load mass data, an error calculation is performed to obtain a first error value, and the first error value is compared with a first error threshold. When the first error value is greater than the first error threshold, a first state feedback parameter is generated to correct the tire-road contact friction force parameter, and a first correction parameter is generated. Based on the steering wheel motor speed data and the motor characteristic parameters, an error calculation is performed to obtain a second error value, and the second error value is compared with the second error threshold. When the second error value is greater than the second error threshold, a second state feedback parameter is generated to correct the torque constant of the motor characteristic parameter, and a second correction parameter is generated. Based on the motor current value data and the motor energy feedback parameters, an error calculation is performed to obtain a third error value, and the third error value is compared with the third error threshold. When the third error value is greater than the third error threshold, a third state feedback parameter is generated to correct the motor equivalent resistance parameter of the motor energy feedback parameter, and a third correction parameter is generated. Based on the first correction parameter, the second correction parameter, and the third correction parameter, combined with the vehicle speed data, the driving of the driverless electric vehicle is integrated to generate the slope driving parameter set.

7. The slope driving control method for an unmanned trolley as described in claim 1, characterized in that, The graded braking control strategy is executed based on the inductive coupling module and the DC voltage control module, including: Based on the anti-slip braking level, the power switch of the inductive coupling module is alternately closed and opened to obtain the anti-slip torque control parameters; Based on the light braking level, the DC voltage control module limits the discharge current parameter to obtain the first braking control parameter; Based on the medium braking level, the operating frequency parameters of the inductive coupling module and the discharge current parameters of the DC voltage control module are coordinated to obtain the second braking control parameters; Based on the emergency braking level, the maximum coupling degree of the inductive coupling module and the discharge current of the DC voltage control module are activated simultaneously to obtain the third braking control parameter. The first braking control parameter, the second braking control parameter, and the third braking control parameter are added to the graded braking control strategy.

8. A ramp driving control system for an unmanned electric vehicle, characterized in that, The steps for implementing the ramp driving control method for the driverless trolley according to any one of claims 1 to 7, wherein the ramp driving control system of the driverless trolley includes: The slope monitoring module is used to measure the slope angle of the vehicle in real time through the inertial measurement unit, and to monitor the driverless trolley based on the slope angle to obtain multiple trolley monitoring data. The parameter determination module is used to construct a single-steering wheel ramp driving dynamics model, and to synchronize the multiple trolley monitoring data to the single-steering wheel ramp driving dynamics model to obtain a ramp driving parameter set. The ramp control module is used to activate the inductive coupling module and the DC voltage control module to coordinate the operation of the driverless trolley according to the ramp driving parameter set, generate a graded braking control strategy, and execute the graded braking control strategy to intelligently control the ramp driving of the driverless trolley, including: Based on the set of ramp driving parameters, operating conditions are identified to determine multiple ramp operating condition types. Based on the multiple ramp conditions, the inductive coupling module and the DC voltage control module are activated to perform braking classification on the driverless trolley, defining multiple braking levels, including: Based on the analysis of the multiple slope working conditions, uphill working conditions and downhill working conditions are obtained; When the slope condition of the driverless electric vehicle is the uphill condition, the inductive coupling module is activated to perform instantaneous torque compensation to obtain the anti-slip braking level and the normal driving level. When the slope condition of the driverless electric vehicle is the downhill condition, the DC voltage control module is activated to perform braking classification, obtaining light braking level, medium braking level, and emergency braking level. Based on the inductive coupling module and the DC voltage control module, a cooperative driving analysis of the driverless electric vehicle is performed, and a cooperative driving analysis result is generated. Based on the cooperative driving analysis results, braking analysis is performed on the anti-slip braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level, and an execution priority sequence is set based on the braking analysis results; The anti-slip braking level, the normal driving level, the light braking level, the moderate braking level, and the emergency braking level are sorted according to the execution priority sequence to define the multiple braking levels; Based on the multiple braking levels, the operation control analysis of the driverless electric vehicle is carried out to construct a graded braking control strategy; The graded braking control strategy is executed based on the inductive coupling module and the DC voltage control module. An inductive coupling module is added to the outside of the motor, and a DC voltage control module is added to the DC input terminal of the motor controller. The inductive coupling module works in conjunction with the motor to switch the stable current through a power switch. The DC voltage control module integrates sampling processing and discharge circuits to dynamically adjust the DC bus voltage.

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