Special vehicle ramp auxiliary braking system and method

Abstract

This invention relates to a hill-start assist braking system and method for special vehicles. The system collects parameters such as slope, clutch status, and engine speed through a sensor module. A controller constructs an engine anti-drag torque estimation model based on the vehicle's longitudinal dynamics model and introduces a dynamic prediction model for engine safe speed based on a radial basis function neural network. When the special vehicle is determined to be going downhill, the gear is determined based on the real-time speed ratio, and the neural network predicts the dynamic safe speed threshold for that gear under the current operating conditions. When the actual engine speed exceeds this dynamic threshold, the controller calculates the target braking deceleration based on a PID algorithm and then outputs a PWM control signal to the electronically controlled pneumatic brake valve through a braking pressure-deceleration mapping model to achieve graded linear braking. This invention solves the cylinder head injury problem caused by engine overspeed in special vehicles under complex high-altitude road conditions, achieving intelligent and precise hill-start assist braking control.

Description

Technical Field

[0001] This invention relates to the field of vehicle intelligent control technology, and more specifically, to a hill-start assist braking system and method for special vehicles. Background Technology

[0002] Special vehicles, especially 8x8 wheeled armored vehicles, often need to travel in complex terrains such as plateaus, mountains, and mountain passes due to their tactical missions and combat environments. These vehicles are characterized by their large combat mass and high inertia. When traveling on long downhill or steep slopes, if the driver operates improperly (such as coasting in neutral for extended periods or mismatching gear and speed), the vehicle will continuously accelerate under the influence of gravity, causing the engine to be dragged by the vehicle's inertia and run at overspeed. This can easily lead to serious mechanical failures such as valve knocking and connecting rod bending, resulting in non-combat casualties and equipment damage.

[0003] In the prior art, there are some solutions for hill-start assist braking. For example, patent CN110303901B proposes a hill-start assist braking method suitable for electric vehicles, which mainly relies on the energy recovery of the motor to generate reverse torque for braking. However, this method is only applicable to electric or hybrid vehicles with regenerative braking function, and cannot be applied to the widely deployed traditional fuel-powered special vehicles. Moreover, its control logic is relatively simple and does not consider the core problem of engine drag overspeed. Patent CN119568093B proposes a braking control method for hybrid vehicles, which adjusts the braking force by predicting future vehicle speed and safe vehicle speed. Although this method introduces a prediction mechanism, its prediction model is mainly based on external environmental factors such as road conditions and weather, without deeply integrating the vehicle's own dynamic characteristics and engine operating status. Furthermore, its braking distribution strategy mainly targets the coordination of active braking (hydraulic) and auxiliary braking (motor / engine), failing to specifically address the specific technical challenge of engine drag overspeed.

[0004] Therefore, there is an urgent need for a hill start assist braking system specifically designed for special vehicles, capable of accurately identifying engine overspeed risks and achieving intelligent active braking, to fill the gap in existing technology. Summary of the Invention

[0005] To address the technical challenges mentioned above, particularly the lack of effective methods to prevent engine overspeed damage in traditional fuel-powered special vehicles under complex downhill conditions, this invention proposes a hill-start assist braking system and method for special vehicles. The specific technical solution is as follows:

[0006] A special vehicle hill-start assist braking system, characterized in that it comprises:

[0007] The multi-source sensor acquisition module includes at least a tilt acceleration sensor, a Hall proximity switch, a CAN bus interface, a Hall speed sensor, and a load sensor, which are used to acquire the vehicle's slope value, clutch status, engine speed, transmission output speed, and the vehicle's current load in real time.

[0008] The controller, based on operating condition recognition for auxiliary braking, is electrically connected to the multi-source sensor acquisition module, the graded alarm module, and the braking execution module, respectively. The controller integrates the following:

[0009] The vehicle longitudinal dynamics model is used to calculate the theoretical acceleration of the vehicle and the engine anti-drag torque when going downhill based on the gradient and vehicle load.

[0010] The engine safe speed dynamic prediction model is trained with historical operating condition data. The input parameters include the current gear, slope value, vehicle load and engine coolant temperature. The output is the dynamic safe speed threshold under the operating condition.

[0011] The PID control algorithm unit is used to calculate the target braking deceleration based on the difference between the actual engine speed and the dynamic safe speed threshold and the rate of change of the difference;

[0012] A braking pressure-deceleration mapping model is used to convert the target braking deceleration into the target braking chamber pressure and generate a corresponding PWM control signal;

[0013] The graded alarm module is used to receive alarm commands from the controller and issue different levels of audible and visual alarms according to the severity of engine overspeed.

[0014] The braking execution module includes a high-speed switching solenoid valve and a pressure sensor, which are used to receive the PWM control signal, perform linear inflation braking on the vehicle's service brake air circuit, and perform closed-loop feedback control through the pressure sensor.

[0015] Compared with the prior art, the beneficial effects of the special vehicle hill-start assist braking system and method described in this invention are mainly reflected in the following aspects:

[0016] 1) Deep integration of physical model and intelligent algorithm: This invention innovatively combines the vehicle longitudinal dynamics model with the RBF neural network prediction model. The dynamics model provides theoretical support, while the neural network dynamically predicts the safe speed threshold adapted to different environments (such as different slopes, loads, and engine thermal states) based on massive historical operating condition data, which is more accurate and intelligent than the single-parameter negative feedback control in the existing technology;

[0017] 2) Highly targeted, addressing core pain points: This invention is specifically designed to address the problem of engine overspeed in traditional fuel-powered special vehicles. All control logic revolves around "preventing engine speed from exceeding the dynamic safety threshold," which is fundamentally different from existing technologies 1 (electric vehicle energy recovery) and 2 (hybrid universal prediction).

[0018] 3) High control precision and smooth braking: The incremental PID algorithm is used to calculate the target deceleration, and the linear and precise control of the braking pressure is achieved through the braking pressure-deceleration mapping model and closed-loop air pressure feedback. This avoids the shock of traditional on / off braking and improves driving smoothness and ride comfort.

[0019] 4) Safety redundancy and behavior recording: The system has a graded alarm function, which can provide different prompts according to the degree of danger; at the same time, all triggering events and key data are recorded, providing valuable data for accident tracing, driving behavior analysis and vehicle health management. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structure of the special vehicle ramp assist braking system of the present invention;

[0021] Figure 2 This is a schematic diagram of the engine safe speed dynamic prediction model based on RBF neural network of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The described embodiments are merely some embodiments of the present invention, and not all embodiments.

[0023] Example 1

[0024] This embodiment provides a special vehicle hill-start assist braking system, particularly suitable for solving the problem of engine damage caused by gear mismatch and engine speed mismatch during long-distance downhill driving of a certain type of 8×8 wheeled armored personnel carrier. By adopting the technical solution of this embodiment, after reaching a certain slope, when the engine is in reverse drag, the auxiliary braking system automatically controls the operation based on the logical relationship between engine speed and gear position, thereby protecting the engine through deceleration.

[0025] Please see Figure 1 The auxiliary braking system mainly includes the following functional modules: multi-source sensor acquisition module 1, auxiliary braking controller based on working condition identification 2, graded alarm module 3, and braking execution module 4.

[0026] Multi-source sensor acquisition module 1 is primarily responsible for collecting real-time vehicle status information to provide decision-making support for the controller. It includes the following components:

[0027] The tilt acceleration sensor (MEMS) boasts extremely high precision and is used to measure the longitudinal slope angle θ of a vehicle in real time. It is installed near the vehicle's center of gravity. Through built-in temperature compensation and Kalman filtering algorithms, the measurement accuracy can reach ±0.1°, enabling accurate determination of whether the vehicle is on a downhill slope and the magnitude of the slope.

[0028] Hall effect proximity switch: Installed at the front end of the clutch master cylinder or slave cylinder push rod. When the clutch is engaged (push rod extended), the switch senses metal and outputs a low-level signal (0); when the clutch is disengaged (push rod retracted), the switch moves away from the metal and outputs a high-level signal (1). Thus, the controller can accurately determine the clutch state S. clutch .

[0029] The CAN bus interface communicates with the vehicle's CAN bus. It obtains the engine's real-time speed n by parsing CAN data messages (such as the J1939 protocol). e (Unit: rpm), Engine coolant temperature T coolant (Unit: °C) and other information.

[0030] A Hall effect speed sensor is used to measure the output shaft speed of a gearbox. It is installed near the output shaft and senses the gear ring on the shaft to determine the speed. The sensor's output frequency is related to the output shaft speed n. out A square wave signal (unit: rpm) proportional to the frequency of the square wave. The controller calculates n by measuring the square wave frequency. out .

[0031] Load sensor: This can be a height sensor or an airbag pressure sensor installed in the suspension system, used to estimate the vehicle's current load m (unit: kg). Load information is crucial for subsequent dynamic calculations because, on the same slope, a loaded vehicle requires significantly more braking force than an empty vehicle.

[0032] The auxiliary braking controller 2, based on operating condition recognition, is the "brain" of the entire auxiliary braking system. Controller 2 includes a processor and a memory electrically connected to it. The memory stores multiple key algorithm models, which can be called by the processor when processing data. These key algorithm models include at least the following:

[0033] (1) Vehicle longitudinal dynamics model:

[0034] This model is used to calculate the forces acting on a vehicle and its theoretical acceleration when going downhill. The force balance equations along the slope direction for a vehicle going downhill are:

[0035]

[0036] Among them, F g = m·g·sinθ is the component of gravity along the slope (slope assist), F brake F is the braking force provided by the braking system. res = F roll + F aero This represents the driving resistance (including rolling resistance and air resistance). Ignoring minor terms, the theoretical acceleration *a* of the vehicle without braking is... theroy =g·sinθ. This model can also be used to estimate the braking force required to maintain the current vehicle speed.

[0037] (2) Dynamic prediction model for engine safe speed based on radial basis function (RBF) neural network:

[0038] This model is one of the core algorithms in this embodiment. As is well known, traditional hill-start assist systems typically set a fixed maximum safe engine speed (e.g., 2000 rpm) for each gear. However, this approach does not consider the actual impact of factors such as vehicle load, gradient, and engine thermal state on the safety threshold. For example, when heavily loaded and descending a steep slope, the risk of engine overspeed is higher, and the safety threshold should be appropriately lowered to allow for earlier braking intervention.

[0039] This embodiment uses an RBF neural network to construct a dynamic prediction model. RBF networks have the advantages of fast learning speed and strong nonlinear approximation ability.

[0040] Please see Figure 2 The input layer of the model contains four nodes: the current gear (calculated from the speed ratio), the gradient θ, the vehicle load m, and the engine coolant temperature T. coolant (Temperature affects engine oil viscosity, which in turn affects internal engine friction resistance, thus altering anti-drag characteristics.) The hidden layer consists of radial basis function neurons, using a Gaussian function as the kernel function. The output layer consists of one node, namely the dynamic safe rotation speed threshold N. safe_dynamic .

[0041] The mathematical expression for the model is:

[0042]

[0043] Where X is the current input 4-dimensional feature vector (gear position, gradient, load, coolant temperature), C i Let ω be the center point of the i-th radial basis function. i is the weight coefficient, and n is the number of hidden layer nodes.

[0044] In the offline phase, the model is trained using a large amount of "operating condition-safe speed" data obtained from real vehicle collection or bench tests, enabling the model to accurately predict the maximum permissible speed at which the engine will not experience cylinder head failure under various complex operating conditions.

[0045] (3) Gear and speed calculation module:

[0046] The controller determines the output speed n of the gearbox. out and engine speed n e Real-time calculation of current speed ratio i g = n e / n out And compare it with the gearbox ratio table to determine the current gear. At the same time, calculate the current vehicle speed υ (unit: km / h):

[0047]

[0048] Where, n engine R is the engine speed, and r is the tire rolling radius (unit: m).

[0049] (4) Incremental PID control algorithm unit:

[0050] When the actual engine speed n e Exceeding the dynamic security threshold N safe_dynamic At that time, the controller calculates the deviation e = n e -N safe_dynamic To achieve smooth and rapid braking control, this embodiment employs an incremental PID algorithm, whose output is the target braking deceleration Δa required for each additional control cycle (e.g., 20ms). target .

[0051]

[0052] The target braking deceleration a in the current cycle target (k)=a target (k−1)+Δa target (k). By adjusting K p K i K d These three parameters can optimize the system's response speed and stability.

[0053] (5) Braking pressure-deceleration mapping model:

[0054] Δa output by the PID controller target The pressure P that needs to be converted into the brake chamber target This model is based on braking dynamics:

[0055]

[0056] Where η is the braking transmission efficiency, BEF is the brake performance factor (characterizing the brake's ability to convert chamber pressure into braking torque), A is the effective area of ​​the brake chamber, μ is the coefficient of friction between the brake pads and the brake drum, and n is the number of brakes involved in braking. These parameters are inherent characteristics of the vehicle model and can be pre-calibrated. The controller calculates P... target It generates a corresponding PWM (Pulse Width Modulation) signal to drive the high-speed switching solenoid valve.

[0057] Upon receiving the control signal from the alarm device, the graded alarm module 3 displays an alarm for excessive engine speed and an alarm for prolonged neutral gear operation, and also issues an audible alert. The graded alarm module 3 includes LED indicator lights and a buzzer. The controller performs graded alarms based on the calculated deviation 'e'.

[0058] Level 1 warning: When e > 0 but less than 200 rpm, the indicator light flashes slowly at a frequency of 1 Hz, and the buzzer emits intermittent short beeps to remind the driver that the brakes may not have engaged or may have only engaged slightly at this time.

[0059] Level 2 alarm: When e ≥ 200rpm, the indicator light flashes rapidly at a frequency of 5Hz and the buzzer sounds continuously, indicating an emergency situation and that the braking system is working at full capacity, reminding the driver to immediately cooperate with braking or take other measures.

[0060] Braking execution module 4 receives a control signal from the auxiliary braking device. The auxiliary braking device controls the air circuit to implement the service brake. This braking air circuit is connected in parallel with the original vehicle service brake air circuit and uses electronically controlled air pressure for braking. Braking execution module 4 mainly includes:

[0061] High-speed switching solenoid valve: Receives PWM signals from the controller. By adjusting the duty cycle of the PWM wave, the opening and closing time ratio of the solenoid valve can be controlled, thereby achieving linear regulation of the brake chamber pressure, similar to a "digital proportional valve".

[0062] Air pressure sensor: Installed at the inlet of the brake chamber, used to detect and report the actual pressure P in real time. actual The controller will P actual With P target By comparing and forming a closed-loop control, the PWM signal is continuously corrected to ensure that the actual pressure accurately follows the target pressure, thereby achieving precise braking.

[0063] By improving the existing vehicle driving control system and adding a hill start assist braking system, the applicable technical specifications are as follows: working angle: ≥3°; braking response time: ≤0.5s; working temperature: -43℃~55℃; working humidity: 20%~80%; altitude: <5200m.

[0064] When special operations vehicles (such as 8x8 wheeled armored vehicles) travel in complex terrains such as plateau passes, mountains, and mountain passes, an auxiliary braking system can be built between the vehicle's driving control system and braking system. This enables special operations vehicles to move intelligently and dynamically adapt to different braking levels in different environments. This not only improves driving safety, but also enhances driving smoothness and passenger comfort by predicting in advance and avoiding obstacles in time. It also provides valuable data for accident tracing, driving behavior analysis, and vehicle health management.

[0065] Based on the above-mentioned auxiliary braking system, this embodiment of the invention further provides a hill-start assist braking method, which specifically includes the following steps:

[0066] Step S201: Multi-source sensor acquisition module 1 acquires slope θ and clutch status S in real time. clutch Engine speed n e , gearbox output speed n out Load capacity (m), coolant temperature (T) coolant ;

[0067] Step S202: Controller 2 determines the downhill state. If θ < -3° (i.e., the downhill angle is greater than 3 degrees) and S clutch =0 (clutch engaged) and n out If the value is >0, the vehicle is determined to be in gear going downhill and enters monitoring mode.

[0068] Step S203: Calculate the current gear and vehicle speed;

[0069] Step S204: Adjust gear position, θ, m, T coolant Inputting the RBF neural network model yields the dynamic safe speed threshold N. safe_dynamic ;

[0070] Step S205: Calculate the deviation e = n e -N safe_dynamic If e > 0, proceed to braking control procedure S206; otherwise, return to S201.

[0071] Step S206: The incremental PID controller calculates the target deceleration a based on e and its historical values. target ;

[0072] Step S207: Calculate the target pressure P based on the braking pressure-deceleration mapping model. target And convert it into a PWM duty cycle signal;

[0073] Step S208: Drive the high-speed switching solenoid valve to brake, and at the same time, the air pressure sensor provides closed-loop feedback;

[0074] Step S209: The graded alarm module 3 issues a corresponding audible and visual alarm based on the magnitude of e.

[0075] Based on the above steps S201~S209, the following steps are further included after the audible and visual alarm:

[0076] Step S210: When the engine speed drops to N safe_dynamic When the speed drops below 100 rpm (hysteresis range), the controller gradually reduces the PWM duty cycle and stops braking.

[0077] Step S211: Store all data of this braking event (time, gradient, load, gear, speed change, braking pressure, etc.) into the ferroelectric memory.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art can make numerous improvements and modifications without departing from the core concept and basic principles of the present invention, and these improvements and modifications to the features and effects should also fall within the protection scope of the present invention.

Claims

1. A hill-start assist braking system for special vehicles, characterized in that, include: The multi-source sensor acquisition module includes at least an tilt acceleration sensor, a Hall effect proximity switch, a CAN bus communication interface, a Hall effect speed sensor for the gearbox output shaft, and a load sensor, which are used to acquire the vehicle's slope value, clutch status, engine speed, gearbox output speed, and current vehicle load in real time. The controller, based on operating condition recognition for auxiliary braking, is electrically connected to the multi-source sensor acquisition module, the graded alarm module, and the braking execution module, respectively. The controller integrates the following: The vehicle longitudinal dynamics model is used to calculate the theoretical acceleration of the vehicle and the engine anti-drag torque when going downhill based on the gradient and vehicle load. The engine safe speed dynamic prediction model takes the current gear, gradient value, vehicle load and engine coolant temperature as input parameters, and outputs the dynamic safe speed threshold under the working condition. The PID control algorithm unit is used to calculate the target braking deceleration based on the difference between the actual engine speed and the dynamic safe speed threshold and the rate of change of the difference; A braking pressure-deceleration mapping model is used to convert the target braking deceleration into the target braking chamber pressure and generate a corresponding PWM control signal; The braking execution module includes a high-speed switching solenoid valve and a pressure sensor, which are used to receive the PWM control signal and perform air inflation braking on the vehicle's service brake air circuit.

2. The special vehicle hill-start assist braking system according to claim 1, characterized in that, The engine safe speed dynamic prediction model based on radial basis function neural network uses a training dataset containing historical data on vehicle overspeed due to reverse drag under different gradients, loads, gears, and engine thermal states. The model takes currently collected operating parameters as input and outputs a dynamic safe speed threshold N adapted to the current operating conditions. safe_dynamic .

3. The special vehicle hill-start assist braking system according to claim 1, characterized in that, The auxiliary braking controller is also used to calculate the vehicle's current speed υ in real time based on the ratio of the transmission output speed to the engine speed, combined with the vehicle's final drive ratio and tire radius. The calculation formula is as follows: ； Where, n engine i represents engine speed. g i is the current gear ratio of the transmission, i0 is the gear ratio of the main reducer, and r is the tire rolling radius.

4. The special vehicle hill-start assist braking system according to claim 1, characterized in that, The braking system includes a graded alarm module, which receives alarm commands from the controller and issues different levels of audible and visual alarms based on the severity of engine overspeed. The auxiliary braking controller is also used to trigger a coasting violation alarm when it detects that the transmission is in neutral and the gradient value is greater than a preset threshold, regardless of the engine speed, and to record the violation event to the internal non-volatile memory.

5. The special vehicle hill-start assist braking system according to claim 1, characterized in that, The PID control algorithm unit adopts an incremental PID control algorithm, and its output is the target braking deceleration a. target The calculation formula is as follows: ; Where e(k) is the deviation between the actual engine speed at the current moment and the dynamic safe speed threshold, and K p K i K d These are the tunable control parameters.

6. A method for hill-start assist braking of special vehicles based on the system described in any one of claims 1 to 5, characterized in that, Includes the following steps: S1: Real-time acquisition of vehicle slope value θ and clutch status S via multi-source sensor acquisition module. clutch Engine speed n e , gearbox output speed n out Vehicle load (m) and engine coolant temperature (T) coolant ; S2: The auxiliary brake controller adjusts according to the clutch status S clutch and gearbox output speed n out To determine if the vehicle is coasting downhill in gear, if S clutch It is a combined state and n out If the value is greater than 0, it is determined to be coasting in gear, and S3 is executed; S3: Based on engine speed n e With the gearbox output speed n out The ratio of the gear ratio i is used to calculate the current gear ratio. g This allows us to determine the current gear. S4: Set the current gear, gradient θ, vehicle load m, and engine coolant temperature T. coolant The input is fed into a dynamic prediction model for engine safe speed based on a radial basis function neural network. The model outputs a dynamic safe speed threshold N adapted to the current operating conditions. safe_dynamic ; S5: Calculate the current engine speed n e With dynamic safety speed threshold N safe_dynamic Deviation e = n e -N safe_dynamic If e > 0, then it is determined that the engine is at risk of overspeeding, and S6 is executed; otherwise, return to S1. S6: Input the deviation e and its rate of change e' into the incremental PID control algorithm unit to calculate the target braking deceleration a. target ; S7: Decelerate the target by a target Inputting the braking pressure-deceleration mapping model and combining it with vehicle dynamics parameters, the target brake chamber pressure P required to achieve this deceleration is calculated. target ; S8: The controller adjusts the target pressure P. target The corresponding PWM control signal is generated to drive the high-speed switching solenoid valve in the braking execution module to perform linear air charging braking on the service brake air circuit. The actual pressure is fed back in real time through the air pressure sensor to form a closed-loop control until the engine speed drops back to a safe range. S9: During braking, the graded alarm module emits audible and visual alarm signals of different frequencies and brightness according to the magnitude of the deviation e.

7. The special vehicle ramp auxiliary braking method according to claim 6, characterized in that, The calculation formula for the engine safe speed dynamic prediction model based on radial basis function neural network in step S4 is as follows: ； Where X is the input feature vector (gear position, gradient, load, coolant temperature), C i Let ω be the center point of the i-th radial basis function. i ϕ is the weighting coefficient, and ϕ is the Gaussian kernel function.

8. The special vehicle ramp auxiliary braking method according to claim 6, characterized in that, The braking pressure-deceleration mapping model in step S7 is based on vehicle braking dynamics, and its calculation formula is as follows: ； Where m is the vehicle load, r is the tire rolling radius, η is the braking transmission efficiency, BEF is the braking efficiency factor, A is the effective area of ​​the brake chamber, μ is the brake friction coefficient, and n is the number of brakes.

9. The special vehicle ramp auxiliary braking method according to claim 6, characterized in that, It also includes step S10: storing the engine speed change curve, brake pressure change curve, slope value, gear position and final control effect during each triggering of auxiliary braking into the ferroelectric memory inside the controller for post-event analysis and driving behavior evaluation.

10. The special vehicle ramp auxiliary braking method according to claim 6, characterized in that, In step S6, when the deviation e is greater than the preset first-level alarm threshold but less than the second-level alarm threshold, the graded alarm module emits low-frequency flashing lights and intermittent buzzing sounds; when e is greater than the second-level alarm threshold, it emits high-frequency flashing lights and continuous buzzing sounds.

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