A deceleration control method, device and vehicle based on real-time quality estimation

Abstract

The application discloses a deceleration control method and device based on real-time quality estimation and a vehicle. The method comprises the following steps: calculating a vehicle quality estimation value under the condition that a quality estimation condition is met; determining a gain scaling factor according to the vehicle quality estimation value; determining first PID parameters according to an ESC working state and the gain scaling factor; determining a PID output pressure according to the first PID parameters; determining a feedforward compensation pressure according to the vehicle quality estimation value; and determining a final braking pressure according to the PID output pressure and the feedforward compensation pressure. Through the cooperation of quality sensing, gain scaling, PID parameter self-adaption and feedforward compensation, the application realizes load-adaptive deceleration control with high precision, high consistency, high robustness and low cost, and significantly improves the braking performance and safety of intelligent electric vehicles and ADAS systems.

Description

Technical Field

[0001] This invention belongs to the field of vehicle technology, specifically relating to a deceleration control method, device, and vehicle based on real-time mass estimation. Background Technology

[0002] In Level 2 and above autonomous driving systems, ADAS (Advanced Driver Assistance Systems) need to generate precise braking pressure requests based on the target deceleration. However, existing technologies typically use a fixed mass calibration method, where the control system uses preset no-load or full-load mass values, which cannot adapt to actual load changes, resulting in excessive braking when no-loaded and insufficient braking when fully loaded. Summary of the Invention

[0003] One object of the present invention is to provide a deceleration control method, device and vehicle based on real-time mass estimation, which can solve the above-mentioned technical problems in the prior art.

[0004] According to a first aspect of the present invention, a deceleration control method based on real-time mass estimation is provided, comprising:

[0005] Calculate the estimated vehicle mass under the condition that the mass estimation conditions are met;

[0006] The gain scaling factor is determined based on the estimated vehicle mass.

[0007] The first PID parameter is determined based on the ESC operating status and the gain scaling factor.

[0008] The PID output pressure is determined based on the first PID parameters;

[0009] The feedforward compensation pressure is determined based on the estimated vehicle mass.

[0010] The final braking pressure is determined based on the PID output pressure and the feedforward compensation pressure.

[0011] Optionally, determining the gain scaling factor based on the estimated vehicle mass includes:

[0012] The gain scaling factor is determined using the following formula:

[0013] ;

[0014] in, This is the gain scaling factor. This is an estimated value for the vehicle's mass. For the vehicle's unloaded weight, These are calibration coefficients.

[0015] Optionally, determining the first PID parameter based on the ESC operating state and the gain scaling factor includes:

[0016] The second PID parameter is determined from the pre-configured set of PID parameters based on the ESC operating status;

[0017] The first PID parameter is obtained by scaling the second PID parameter using the gain scaling factor.

[0018] Optionally, determining the second PID parameter from a pre-configured set of PID parameters based on the ESC operating state includes:

[0019] If ESC is in normal condition, the proportional term Kp in the second PID parameter is 15.0, the integral term Ki is 0.75, and the derivative term Kd is 0.1.

[0020] If the ESC is in a fault state, the proportional term Kp in the second PID parameter is 10.0, the integral term Ki is 0.5, and the derivative term Kd is 0.03.

[0021] Optionally, determining the feedforward compensation pressure based on the estimated vehicle mass includes:

[0022] Calculate the feedforward compensation pressure using the following formula:

[0023] ;

[0024] in, To compensate for the pressure of feedforward, To slow down the target, This is the pressure-deceleration conversion coefficient. This is the feedforward ratio coefficient. This is an estimated value for the vehicle's mass. This refers to the vehicle's unloaded weight.

[0025] Optionally, the feedforward ratio is 0.85.

[0026] Optionally, the calculation of the estimated vehicle mass includes:

[0027] Calculate the estimated vehicle mass using the following formula:

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] in, This is an estimated value for the vehicle's mass. For wheel-end driving force, This refers to the wheel end torque. For the conversion gain between driving force and torque, For braking force, For the wheel cylinder pressure, The gain for the conversion between braking force and wheel cylinder pressure, For air resistance, The air drag coefficient, For vehicle speed, To decelerate, This is friction.

[0033] Optionally, the mass estimation conditions include: vehicle speed within the range of 10-80 km / h and absolute deceleration greater than 1.0 m / s². 2 ABS and ESC not activated, no signal fault.

[0034] According to a second aspect of the present invention, an electronic device is provided, including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of a deceleration control method based on real-time mass estimation as described in the first aspect of the present invention.

[0035] According to a third aspect of the present invention, a vehicle is provided, including an electronic device as described in the second aspect of the present invention.

[0036] The beneficial effects of this invention are as follows: By employing real-time mass estimation, gain scaling, feedforward compensation, and PID closed-loop control, this invention enables the system to accurately match pressure requirements under different loads. The mass estimation module adds only a small amount of processor load, making it suitable for real-time operation on embedded MCUs (Microcontroller Units) without requiring additional high-cost hardware. Consistent braking performance is guaranteed regardless of any intermediate load from no-load to full-load, or in normal or faulty actuator states. This solution achieves high-precision, high-consistency, high-robustness, and low-overhead load-adaptive deceleration control through the synergy of mass sensing, gain scaling, PID parameter adaptation, and feedforward compensation, significantly improving the braking performance and safety of intelligent electric vehicles and ADAS systems. Attached Figure Description

[0037] Figure 1 This is a flowchart of a deceleration control method based on real-time mass estimation in an embodiment of the present invention. Detailed Implementation

[0038] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0039] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0040] Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0041] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0042] In the specification of this invention, the terms "first" and "second" may explicitly or implicitly include one or more of the same feature. In the description of this invention, unless otherwise stated, "multiple" means two or more. Furthermore, in the specification, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0043] In Level 2 and higher autonomous driving systems, ADAS needs to generate precise braking pressure requests based on the target deceleration. However, existing technologies typically suffer from the following problems:

[0044] (1) Fixed mass calibration: The control system uses preset no-load or full-load mass values, which cannot adapt to actual load changes (such as the number of passengers or the weight of goods), resulting in excessive braking when no-load (deceleration overshoot) and insufficient braking when full-load (deceleration undershoot).

[0045] (2) Open-loop feedforward-based: Most systems rely only on the static mapping of "deceleration-pressure" and ignore the differences in vehicle dynamics;

[0046] (3) No quality feedback mechanism: Even if a quality estimation module is available, it is not integrated into the PID (Proportional Integral Derivative) control parameters in real time, resulting in control gain mismatch;

[0047] (4) Actuator status not considered: Normal PID parameters are still used in scenarios such as ESC (Electronic Stability Control) pressure build-up failure, which reduces robustness.

[0048] like Figure 1 As shown in the figure, this embodiment introduces a deceleration control method based on real-time mass estimation, including steps 1100-1600.

[0049] Step 1100: Calculate the estimated vehicle mass if the mass estimation conditions are met.

[0050] The mass estimate is calculated based on Newton's laws, taking into account the vehicle's wheel-end driving force, braking force, air resistance, and friction.

[0051] The conditions for mass estimation include: vehicle speed within the range of 10-80 km / h and absolute deceleration greater than 1.0 m / s². 2 ABS and ESC not activated, no signal fault.

[0052] When the vehicle speed is less than 10 km / h, the low speed indicates that the vehicle may be in conditions such as starting, stopping, or creeping. The vehicle deceleration signal will have high noise and may be affected by various unsteady behaviors. Additionally, air resistance is negligible at low speeds, but the measurement accuracy of driving force and braking force is relatively low.

[0053] When the vehicle speed exceeds 80 km / h, the speed is high, and air resistance becomes the dominant factor. At this speed, the model parameters are prone to error, and even small force deviations at high speeds can lead to large fluctuations in mass estimation. At the same time, high-speed braking may trigger ABS (Anti-lock Braking System) or other stability systems.

[0054] By limiting the mass estimation to the medium-speed stable region, all forces considered in the mass estimation are within a measurable range with small model errors, thus ensuring the reliability of the mass estimation.

[0055] If the deceleration is too small, such as during gentle braking, the signal-to-noise ratio of the deceleration signal will be low. Sensor noise, slight road slopes, and wind disturbances will significantly interfere with the relationship between force and deceleration, leading to drastic fluctuations in mass estimation.

[0056] The absolute value of the deceleration is limited to greater than 1.0 m / s². 2 The range is only updated under conditions of high deceleration, when braking force dominates, signal strength is high, estimation results are not sensitive to noise, and stability is good.

[0057] When ABS is activated, the braking force is modulated at high frequency (pressure boosting-pressure holding-pressure decompression cycle), the wheel speed fluctuates greatly, and the deceleration of the whole vehicle is not solely determined by the continuous braking force; when ESC is activated, it may be accompanied by single-wheel braking and yaw control, at which time the vehicle motion does not conform to the assumption of the longitudinal single degree of freedom model.

[0058] Mass estimation should be performed when ABS and ESC are not activated to avoid estimating mass under conditions of unstable braking force, high tire slip ratio, and drastic changes in road surface adhesion, thus preventing erroneous mass values ​​from entering the controller.

[0059] If the sensors or communication signals on which the mass estimation relies, such as wheel-end torque, braking pressure, vehicle speed, and deceleration, malfunction (e.g., CAN timeout, signal verification error, sensor failure), it will directly lead to estimation errors.

[0060] Quality estimation is performed in the absence of signal faults to ensure that quality updates only occur when all input signals are valid, preventing flyaway values ​​or abnormal quality due to signal errors.

[0061] Step 1200: Determine the gain scaling factor based on the estimated vehicle mass.

[0062] The gain scaling factor is used to map the real-time estimated vehicle mass to the gain adjustment coefficient of the PID controller, enabling the system to adapt to changes in vehicle load and thus maintain consistent deceleration control performance under different load conditions.

[0063] Specifically, step 1200 includes:

[0064] The gain scaling factor is determined using the following formula:

[0065] ;

[0066] in, This is the gain scaling factor. This is an estimated value for the vehicle's mass. For the vehicle's unloaded weight, These are calibration coefficients.

[0067] When the vehicle is unloaded, the gain scaling factor is 1.0. As the vehicle's mass increases, the gain scaling factor also increases, and the gain scaling factor is linearly proportional to the mass deviation.

[0068] In traditional control schemes, PID parameters are typically calibrated according to a fixed mass. The gain scaling factor mechanism in this application, by sensing the mass in real time and adjusting the gain, ensures that the control system perceives the object characteristics consistently under different loads, thereby outputting a consistent braking pressure-deceleration relationship.

[0069] Step 1300: Determine the first PID parameter based on the ESC operating status and the gain scaling factor.

[0070] When an ESC pressure build-up failure occurs, the maximum pressure build-up rate decreases, making it impossible to quickly increase the braking pressure to the target value. Pressure response hysteresis increases, and the delay from pressure request to actual pressure establishment becomes longer. The maximum achievable pressure is limited, and it may be unable to achieve the high-pressure capability of normal mode. At this time, the actuator is equivalent to a low-bandwidth, slow-response, and weak-capacity system.

[0071] The output of a PID controller is an ideal pressure request calculated based on the deceleration deviation. A high proportional term requires the actuator to output large pressure changes quickly, but the actual pressure rise cannot keep up, resulting in a lag response and the system exhibiting a "sluggish-overshoot" cycle.

[0072] Because the actual pressure does not meet the demand, the deceleration deviation persists, and the integral term accumulates, leading to integral saturation. When the deviation finally decreases (for example, after the pressure slowly rises), the integral term still remains high, causing pressure overshoot or even oscillation.

[0073] High differential terms amplify pressure change rate noise, which can easily cause high-frequency chatter in low-bandwidth actuators.

[0074] Multiple sets of different PID parameters can be pre-configured, corresponding to different ESC states. Based on the ESC operating state, the corresponding PID parameter is selected by looking up a table, and then the PID parameter obtained from the lookup table is scaled using a gain scaling factor to obtain the first PID parameter.

[0075] Step 1400: Determine the PID output pressure based on the first PID parameters.

[0076] Input the first PID parameter into the PID controller to obtain the PID output pressure.

[0077] Step 1500: Determine the feedforward compensation pressure based on the estimated vehicle mass.

[0078] Feedforward compensation pressure is an open-loop control variable that directly calculates a target braking pressure based on the target deceleration and the vehicle's current state (such as load, braking system characteristics, etc.) without waiting for the deviation between the actual deceleration and the target to occur.

[0079] Feedforward compensation pressure can significantly accelerate the response speed. When the target deceleration changes abruptly (e.g., ADAS suddenly requires emergency braking), the feedforward compensation pressure provides most of the required pressure almost simultaneously, without waiting for the actual deceleration to decline before the PID gradually integrates. This avoids the slow pressure ramp-up process in traditional feedback control.

[0080] Feedforward compensation pressure provides coarse-tuning pressure, and the PID only needs fine-tuning within the remaining small range of error. This allows the PID gain to be set smaller, thereby reducing the risk of overshoot and oscillation.

[0081] The feedforward compensation pressure automatically increases or decreases with the vehicle's weight. The greater the load, the higher the feedforward compensation pressure, and the lower it is when the vehicle is unloaded. This ensures that the open-loop portion is roughly matched to the current physical requirements, and the feedback only needs to compensate for residual errors.

[0082] Step 1600: Determine the final braking pressure based on the PID output pressure and the feedforward compensation pressure.

[0083] The final braking pressure is the superposition of the feedforward compensation pressure and the PID output pressure. This ensures both rapid response (no need to wait for deviations to appear) and accurate tracking (closed-loop elimination of residual error). The superimposed final pressure request is executed by the actuator, achieving smooth, accurate, and adaptive braking deceleration control under all operating conditions.

[0084] This embodiment enables the system to accurately match pressure requirements under different loads through real-time mass estimation, gain scaling, feedforward compensation, and PID closed-loop control. The mass estimation module adds only a small amount of processor load, making it suitable for real-time operation on embedded MCUs (Microcontroller Units) without requiring additional high-cost hardware. Consistent braking performance is guaranteed across any intermediate load from no-load to full-load, and in normal or faulty actuator states. This solution achieves high-precision, high-consistency, high-robustness, and low-overhead load-adaptive deceleration control through the synergy of mass sensing, gain scaling, PID parameter adaptation, and feedforward compensation, significantly improving the braking performance and safety of intelligent electric vehicles and ADAS systems.

[0085] In this embodiment, step 1300 includes steps 1310-1320.

[0086] Step 1310: Determine the second PID parameter from the pre-configured set of PID parameters based on the ESC operating status.

[0087] Multiple sets of PID parameters are pre-configured, and the corresponding PID parameters are selected based on the ESC operating state. PID parameters include proportional, integral, and derivative terms. ESC operating states include normal and fault states. The PID parameters corresponding to the normal ESC state are different from those corresponding to the fault state. When the ESC is in a fault state, the PID gain is reduced.

[0088] Specifically, if the ESC is in a normal state, the proportional term Kp in the second PID parameter is 15.0, the integral term Ki is 0.75, and the derivative term Kd is 0.1; if the ESC is in a fault state, the proportional term Kp in the second PID parameter is 10.0, the integral term Ki is 0.5, and the derivative term Kd is 0.03.

[0089] Step 1320: Scale the second PID parameter using the gain scaling factor to obtain the first PID parameter.

[0090] The gain scaling factor only scales the proportional and integral terms, without changing the derivative term. The derivative output is proportional to the rate of change of the deceleration deviation, mainly reflecting the trend of the error signal, and acts as a damper to suppress overshoot and oscillation.

[0091] When the vehicle load changes, the magnitude of the deviation itself is related to the mass (the deceleration is smaller under the same pressure when fully loaded), but the rate of change of the deviation (i.e. how fast the deceleration deviation increases or decreases per unit time) has a very weak direct relationship with the mass.

[0092] From a dynamic perspective, changes in mass alter the static ratio between force and acceleration, not the dynamic slope of the acceleration's response to time. Scaling the differential term as well would excessively amplify high-frequency noise and disrupt the original damping match of the differential term.

[0093] For example, the second PID parameter determined based on the ESC operating status includes a proportional term. Integral terms Differential term After gain scaling factor After scaling, the proportional term of the first PID parameter is: The integral term of the first PID parameter is The derivative term of the first PID parameter is .

[0094] In this embodiment, step 1500 includes:

[0095] Calculate the feedforward compensation pressure using the following formula:

[0096] ;

[0097] in, To compensate for the pressure of feedforward, To slow down the target, This is the pressure-deceleration conversion coefficient. This is the feedforward ratio coefficient. This is an estimated value for the vehicle's mass. This represents the vehicle's unloaded mass. The feedforward ratio is 0.85.

[0098] The target deceleration is generated by the Advanced Driver Assistance System (ADAS) based on environmental perception (distance to the vehicle ahead, relative speed, speed limit, etc.). As the input to feedforward control, the target deceleration directly determines the desired braking intensity. A larger target deceleration results in higher feedforward compensation pressure, enabling the vehicle to quickly establish the required braking force. When the target deceleration changes a step, the feedforward pressure changes a step simultaneously, independent of feedback delay, thus achieving a rapid response.

[0099] The pressure-deceleration conversion factor is obtained based on the physical characteristics of the vehicle braking system, typically through real vehicle or bench testing. Under no-load conditions, different braking pressures are applied, steady-state deceleration is measured, and a pressure-deceleration curve is plotted, with the slope of the linear segment being taken. The pressure-deceleration conversion factor is used to convert the target deceleration into the reference braking pressure required under no-load conditions; this factor comprehensively reflects the force transmission efficiency of the brakes and tires.

[0100] The feedforward proportion coefficient is a constant calibrated by control engineering after balancing response speed and steady-state accuracy, typically between 0.7 and 0.95. This application sets the feedforward proportion coefficient to 0.85, meaning the feedforward contributes 85% of the pressure, with the remaining 15% compensated by the PID closed-loop. The feedforward proportion coefficient controls the weight of the feedforward path in the total control quantity, avoiding complete reliance on the model (which inevitably contains errors) and preserving sufficient closed-loop adjustment space. A higher feedforward proportion coefficient ensures that most of the pressure is provided in advance, resulting in low system latency. Simultaneously, 15% PID adjustment capability is retained to compensate for factors that the feedforward model cannot accurately model, such as brake wear, temperature changes, and road surface adhesion fluctuations. With the feedforward bearing the main pressure, the PID only requires minor adjustments, avoiding overshoot or oscillation caused by high-gain feedback.

[0101] quality factor The pressure increases linearly with the load, automatically providing a higher reference pressure when fully loaded and lowering the reference pressure when unloaded.

[0102] In this embodiment, the feedforward compensation pressure is directly calculated based on the target deceleration, the estimated vehicle mass, and the calibrated pressure-deceleration conversion coefficient. It does not require waiting for the deceleration deviation to occur and directly generates most of the required pressure based on the target, thus eliminating the inherent delay of pure feedback control.

[0103] In this embodiment, the estimated vehicle mass is calculated according to the following formula:

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] in, This is an estimated value for the vehicle's mass. For wheel-end driving force, This refers to the wheel end torque. For the conversion gain between driving force and torque, For braking force, For the wheel cylinder pressure, The gain for the conversion between braking force and wheel cylinder pressure, For air resistance, The air drag coefficient, For vehicle speed, To decelerate, This is friction.

[0109] Wheel-end torque is provided by the motor or engine control unit and is typically the total torque on the drive shaft, already transmitted to the wheel ends via the gearbox and final drive. The conversion gain between driving force and torque is a calibrated value, usually the reciprocal of the tire rolling radius, and can be calibrated based on the actual vehicle tires.

[0110] Wheel cylinder pressure can be obtained through wheel cylinder pressure sensors. The conversion gain between braking force and wheel cylinder pressure is obtained through brake bench testing and calibration, taking into account multiple parameters such as piston area, effective radius of brake disc, brake pad friction, and tire rolling radius.

[0111] The air drag coefficient can be calibrated through wind tunnel testing and is related to the vehicle's frontal area.

[0112] Friction includes rolling resistance and other resistances, which compensate for longitudinal resistances other than air resistance, braking force, and wheel-end driving force, thereby improving the accuracy of mass estimation.

[0113] After the weight estimation is performed, the estimated vehicle weight can be low-pass filtered and clamped. The cutoff frequency of the low-pass filter is 0.0002Hz, and the clamping range is 1200-2000kg.

[0114] Single-shot quality estimations are affected by sensor noise, minor road surface disturbances, and model errors, resulting in high-frequency random fluctuations. Low-pass filtering removes these high-frequency components, leaving the output quality value almost unchanged.

[0115] Even if the estimation formula outputs a jump value due to a transient anomaly (such as a brief gust of wind or slope), the filtered output can only change at a very slow rate and will not cause a sudden change in the control gain.

[0116] Vehicle load (passenger boarding and alighting, cargo loading and unloading) is a slowly changing process, with a time scale of minutes to hours. A 0.0002Hz filter is perfectly matched to this slowly changing characteristic, without responding to the tiny dynamic load fluctuations during each acceleration and deceleration.

[0117] Because the gain scaling factor directly affects the PID gain and feedforward compensation pressure, frequent fluctuations in quality can lead to gain jitter, affecting braking smoothness. The filtered quality output is extremely smooth, ensuring long-term stability of control parameters.

[0118] When a vehicle load undergoes a significant change (such as an instantaneous addition of 400kg of cargo), the mass estimate may take several tens of minutes to gradually catch up to the new value. However, in actual driving, load changes do not occur instantaneously during operation (loading and unloading while stopped), so this delay is acceptable. If immediate effect after loading is required, the filter can be reset by stopping or manually triggering the reset button.

[0119] The lower limit of the clamping limit is usually the vehicle's unloaded weight; the vehicle's weight cannot be less than its unloaded weight. The upper limit of the clamping limit is usually the vehicle's fully loaded weight; exceeding this value indicates that the vehicle is overloaded or that the measurement was incorrect.

[0120] This embodiment introduces an electronic device, including a processor and a memory, wherein the memory stores a program or instructions that can run on the processor, and when the program or instructions are executed by the processor, implement the steps of a deceleration control method based on real-time mass estimation as described in any embodiment of the present invention.

[0121] This embodiment describes a vehicle that includes an electronic device as described in the above embodiments of the present invention.

[0122] While specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention.

[0123] Those skilled in the art will recognize that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0125] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0126] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0127] In addition, the functional modules in the embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0128] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0129] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0130] It should be understood that the sequence numbers of the steps in the invention's content and embodiments do not absolutely imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The foregoing description of embodiments of this disclosure has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this disclosure to the exact form disclosed. Various modifications and variations may exist based on the foregoing teachings, or various modifications and variations may be derived from the practice of this disclosure. These embodiments were chosen and described to illustrate the principles of this disclosure and its practical application, so that those skilled in the art can utilize this disclosure in various implementations and modifications suitable for the specific purpose of the concept.

Claims

1. A deceleration control method based on real-time mass estimation, characterized in that, include: Calculate the estimated vehicle mass under the condition that the mass estimation conditions are met; The gain scaling factor is determined based on the estimated vehicle mass. The first PID parameter is determined based on the ESC operating status and the gain scaling factor. The PID output pressure is determined based on the first PID parameters; The feedforward compensation pressure is determined based on the estimated vehicle mass. The final braking pressure is determined based on the PID output pressure and the feedforward compensation pressure.

2. The deceleration control method based on real-time mass estimation according to claim 1, characterized in that, Determining the gain scaling factor based on the estimated vehicle mass includes: The gain scaling factor is determined using the following formula: ； in, This is the gain scaling factor. This is an estimated value for the vehicle's mass. For the vehicle's unloaded weight, These are calibration coefficients.

3. The deceleration control method based on real-time mass estimation according to claim 1, characterized in that, The step of determining the first PID parameter based on the ESC operating state and the gain scaling factor includes: The second PID parameter is determined from the pre-configured set of PID parameters based on the ESC operating status; The first PID parameter is obtained by scaling the second PID parameter using the gain scaling factor.

4. The deceleration control method based on real-time mass estimation according to claim 3, characterized in that, The step of determining the second PID parameter from a pre-configured set of PID parameters based on the ESC operating state includes: If ESC is in normal condition, the proportional term Kp in the second PID parameter is 15.0, the integral term Ki is 0.75, and the derivative term Kd is 0.

1. If the ESC is in a fault state, the proportional term Kp in the second PID parameter is 10.0, the integral term Ki is 0.5, and the derivative term Kd is 0.

03.

5. The deceleration control method based on real-time mass estimation according to claim 1, characterized in that, The step of determining the feedforward compensation pressure based on the estimated vehicle mass includes: Calculate the feedforward compensation pressure using the following formula: ； in, To compensate for the pressure of feedforward, To slow down the target, This is the pressure-deceleration conversion coefficient. This is the feedforward ratio coefficient. This is an estimated value for the vehicle's mass. This refers to the vehicle's unloaded weight.

6. The deceleration control method based on real-time mass estimation according to claim 5, characterized in that, The feedforward ratio is 0.

85.

7. The deceleration control method based on real-time mass estimation according to claim 1, characterized in that, The calculation of the estimated vehicle mass includes: Calculate the estimated vehicle mass using the following formula: ； ； ； ； in, This is an estimated value for the vehicle's mass. For wheel-end driving force, This refers to the wheel end torque. For the conversion gain between driving force and torque, For braking force, For the wheel cylinder pressure, The gain for the conversion between braking force and wheel cylinder pressure, For air resistance, The air drag coefficient, For vehicle speed, To decelerate, This is friction.

8. The deceleration control method based on real-time mass estimation according to claim 1, characterized in that, The mass estimation conditions include: vehicle speed within the range of 10-80 km / h and absolute deceleration greater than 1.0 m / s². 2 ABS and ESC not activated, no signal fault.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of a deceleration control method based on real-time mass estimation as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, Includes an electronic device as described in claim 9.

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