Vehicle starting control method and related equipment

Abstract

The invention discloses a vehicle starting control method and related equipment, and relates to the technical field of electric vehicles, the method comprises the following steps: acquiring an accelerator pedal opening signal and N types of sensor data in the starting process of a target vehicle; performing confidence fusion processing based on the N types of sensor data to obtain real-time gradient information; according to the accelerator pedal opening signal and the real-time gradient information, the target rotating speed of the target vehicle is determined; determining the motor output torque of the target vehicle based on the deviation value of the target rotating speed and the current rotating speed; and controlling the target vehicle to run with the motor output torque. The gradient sensing precision is improved through multi-sensor fusion, target rotating speed self-adaptive control is achieved in combination with pedal and gradient information, the starting process is adjusted and optimized through closed-loop torque, and therefore the starting safety, smoothness and energy efficiency of the electric automobile can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and more specifically, to a vehicle starting control method and related equipment. Background Art

[0002] With the rapid development of new energy vehicle technology, electric vehicles are increasingly being used for urban commuting, hill driving, and complex road conditions. During the vehicle's launch phase, especially on a hill, the motor's output response and overall vehicle control strategy have a decisive impact on driving smoothness, safety, and overall vehicle energy efficiency. However, electric vehicle launch control faces complex and changing road environments and driving behaviors. Key parameters such as speed response and torque regulation vary significantly under different operating conditions, placing higher demands on the control system's perception accuracy and responsiveness.

[0003] In existing technologies, vehicle launch control typically relies on a single sensor input for slope determination or speed control, lacking the fusion processing of multi-source information. This results in limited slope recognition accuracy and susceptibility to environmental noise interference. Furthermore, the target speed is typically set based on a preset model or fixed algorithm, making it difficult to effectively adapt to different driving intentions and terrain changes, and prone to problems such as delayed power response, start-up judder, or vehicle roll. Especially with the development trend of increasingly complex starting scenarios and faster response times for electric drive systems, traditional launch control methods suffer from low accuracy, slow response, and poor adaptability, making it difficult to meet the comprehensive control requirements of smart electric vehicles for "stable, accurate, and fast" during the launch phase. In other words, the related technologies suffer from technical issues such as poor safety and comfort during vehicle launch. Summary of the Invention

[0004] The Summary of the Invention section of this application introduces a series of simplified concepts that will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] The vehicle starting control method and related equipment provided in this application can improve the slope perception accuracy through multi-sensor fusion, realize target speed adaptive control by combining pedal and slope information, and use closed-loop torque regulation to optimize the starting process, thereby improving the safety, smoothness and energy efficiency of electric vehicle starting.

[0006] In a first aspect, the present application provides a vehicle starting control method, comprising: obtaining an accelerator pedal opening signal and N types of sensor data during the starting process of a target vehicle, wherein N is a natural number greater than 1; performing confidence fusion processing based on the N types of sensor data to obtain real-time slope information; determining a target speed of the target vehicle based on the accelerator pedal opening signal and the real-time slope information; determining the motor output torque of the target vehicle based on a deviation value between the target speed and the current speed; and controlling the target vehicle to travel at the motor output torque.

[0007] In some embodiments, the confidence fusion processing based on the N types of sensor data to obtain real-time slope information includes: determining slope mean information and N initial slope information based on the N types of sensor data, wherein there is a one-to-one correspondence between the initial slope information and the sensor data; calculating the absolute deviation between each initial slope information and the slope mean information; when the absolute deviation of any initial slope information is less than or equal to a preset deviation threshold, updating the confidence weight corresponding to the initial slope information to a first preset weight; when the absolute deviation of any initial slope information is greater than the preset deviation threshold, updating the confidence weight corresponding to the initial slope information to a second preset weight, wherein the second preset weight is less than the first preset weight; based on the updated N confidence weights, weighted fusion of the N initial slope information is performed to obtain the real-time slope information.

[0008] In some embodiments, the N initial slope information is weightedly fused based on the updated N confidence weights to obtain the real-time slope information, including: obtaining resonance intensity data of the sensor corresponding to each initial slope information; determining N resonance correction coefficients corresponding to the N initial slope information based on the resonance intensity data, wherein the resonance correction coefficient is negatively correlated with the resonance intensity data; and the N initial slope information is weightedly fused based on the updated N confidence weights and the N resonance correction coefficients to obtain the real-time slope information.

[0009] In some embodiments, determining the target speed of the target vehicle based on the accelerator pedal opening signal and the real-time slope information includes: determining a slope compensation coefficient based on the real-time slope information; determining a target speed mapping curve based on the driving mode of the target vehicle; obtaining a basic target speed by querying the target speed mapping curve based on the accelerator pedal opening signal; and determining the target speed of the target vehicle based on the basic target speed and the slope compensation coefficient.

[0010] In some embodiments, the vehicle starting control method further includes: obtaining a real-time tire slip rate of the target vehicle; determining a slip rate correction coefficient based on the real-time tire slip rate of the target vehicle; and determining the target speed of the target vehicle according to the basic target speed and the slope compensation coefficient, including: multiplying the basic target speed, the slope compensation coefficient and the slip rate correction coefficient to obtain the target speed.

[0011] In some embodiments, determining the slip rate correction coefficient based on the real-time slip rate of the tire of the target vehicle includes: when the real-time slip rate of the tire is less than a first slip threshold, determining the slip rate correction coefficient to be 1; when the real-time slip rate of the tire is greater than or equal to the first slip threshold and less than a second slip threshold, determining the slip rate correction coefficient to be a number less than 1 and greater than a preset coefficient, and the slip rate correction coefficient is negatively correlated with the real-time slip rate of the tire; when the real-time slip rate of the tire is greater than or equal to the second slip threshold, determining the slip rate correction coefficient to be the preset coefficient.

[0012] In some embodiments, before controlling the target vehicle to travel with the motor output torque, the vehicle starting control method further includes: extracting the peak points and trough points of the speed change curve of the target vehicle; when the number of the trough points exceeds a preset number threshold and the values ​​of all the trough points are less than a preset speed threshold, generating a starting jitter determination signal; in response to the starting jitter determination signal, reducing the motor output torque by a preset torque compensation value.

[0013] In the second aspect, the present application also provides a vehicle starting control device, comprising: a data acquisition unit, used to obtain an accelerator pedal opening signal and N types of sensor data during the starting process of the target vehicle, wherein N is a natural number greater than 1; a slope determination unit, used to perform confidence fusion processing based on the N types of sensor data to obtain real-time slope information; a speed determination unit, used to determine the target speed of the target vehicle based on the accelerator pedal opening signal and the real-time slope information; a torque determination unit, used to determine the motor output torque of the target vehicle based on the deviation value between the target speed and the current speed; and a vehicle starting unit, used to control the target vehicle to travel at the motor output torque.

[0014] In a third aspect, the present application further provides an electronic device comprising: a memory and a processor, wherein the processor is configured to implement the steps of the vehicle starting control method described in the first aspect when executing a computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the vehicle starting control method described in the first aspect.

[0016] In a fifth aspect, the present application also provides a computer program product, including a computer program or computer executable instructions, which, when executed by a processor, implements the vehicle starting control method provided in an embodiment of the present application.

[0017] In summary, this application introduces multiple sensors and performs confidence fusion processing to more accurately identify the current slope information of the vehicle. Compared with the traditional method of relying on a single sensor, it has stronger anti-interference ability and environmental adaptability, can effectively improve the accuracy of slope perception, and provide more reliable input for subsequent control, especially when starting uphill, it can significantly reduce the risk of vehicle sliding downhill; the target speed is calculated based on the accelerator pedal opening and the real-time slope, so as to more accurately reflect the driver's starting needs and the current road conditions, and can achieve a high degree of matching between power output and driving intentions, thereby improving the smoothness and comfort of vehicle starting; by comparing the target speed with the current actual speed, the motor output torque is dynamically adjusted to achieve closed-loop fine control, which can effectively suppress speed fluctuations and energy waste during the starting process, and prevent starting jitter caused by torque overshoot or hysteresis, thereby significantly improving starting stability and energy efficiency performance. In summary, the vehicle starting control method provided in this application improves the slope perception accuracy through multi-sensor fusion, combines pedal and slope information to achieve adaptive control of the target speed, and uses closed-loop torque regulation to optimize the starting process, thereby improving the safety, smoothness and energy efficiency of electric vehicle starting. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0019] Figure 1 A schematic flow chart of a vehicle starting control method provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the structure of a vehicle starting control device provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] Terms in the specification, claims, and drawings of this application, such as "first," "second," "third," "fourth," and the like (if any), are used to distinguish between similar objects, rather than to describe a particular order or precedence. Therefore, it is understood that these terms can be used interchangeably where appropriate, so that the embodiments described can be implemented in a different order, unless otherwise specified in the drawings or descriptions. In addition, the terms "is" and "has" and any variations thereof in this application are intended to cover all possible constituent elements on a non-exclusive basis. For example, a process, method, system, product, or apparatus that includes several steps or units is not necessarily limited to the steps or units that are explicitly listed, but may also include other steps or units that are not explicitly listed, or steps or units that are inherent to the process, method, product, or apparatus.

[0023] In this application, a "module" or "unit" refers to a computer program or part of a computer program that has a specific function and works in conjunction with other related parts to achieve a predetermined goal. These modules or units can be implemented by software, hardware (such as processing circuits or memories), or a combination of the two. One or more processors or memories can implement one or more modules or units. At the same time, each module or unit can also be part of a larger module or unit.

[0024] The technical solutions in this application will be described in detail below in conjunction with the accompanying drawings in the embodiments. It should be noted that the embodiments described are only part of this application, not all embodiments. In the following description, the "some embodiments" mentioned are only a subset of all possible embodiments, which may be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0025] Figure 1 This is a flow chart of a vehicle start control method provided by an embodiment of the present application. Figure 1 The vehicle starting control method provided in the embodiment of the present application may include the following steps 101 to 105:

[0026] Step 101, obtaining an accelerator pedal opening signal and N types of sensor data during the starting process of the target vehicle, where N is a natural number greater than 1;

[0027] In some examples, the target vehicle is an electric vehicle currently executing a launch control strategy. The target vehicle can be an intelligent vehicle equipped with an electric drive system, such as a pure electric or hybrid powertrain, and equipped with an electronic pedal signal acquisition module and a multi-sensor platform. The accelerator pedal position signal indicates how deeply the driver depresses the accelerator pedal. It can be an analog or digital value ranging from 0% to 100%, reflecting the driver's desired starting power. This signal can be acquired in real time by the accelerator pedal position sensor. For example, if the driver lightly depresses the accelerator pedal and a detected 15% position indicates a moderate start demand, while an position above 60% indicates a strong acceleration intention. N types of sensor data are collected by various sensors to estimate information such as the vehicle's slope, attitude, acceleration, and environmental changes. These sensors may include an inertial measurement unit (IMU) to obtain the vehicle's longitudinal and lateral accelerations and angular velocities; an accelerometer to provide the direction of gravity and used for slope estimation; wheel speed sensors to reflect wheel speed and motion; a Global Navigation Satellite System (GNSS) and altimeter to estimate terrain slope changes; and a gyroscope to sense changes in vehicle attitude and assist in determining slope direction. These sensors transmit their raw data to the vehicle's central control unit via the CAN bus, LIN bus, or Ethernet interface. N represents the number of sensor types involved in the fusion of slope estimation and must be at least two for effective multi-source fusion.

[0028] For example, when starting, the vehicle controller receives a 20% opening signal from the accelerator pedal and preliminarily determines that the driver's intention is to start slowly; at the same time, the IMU detects that the vehicle has an upward slope trend of 5°, and the acceleration sensor and wheel speed sensor provide consistent low-speed state information; after fusing the three types of sensor data, it is confirmed that the vehicle is currently in an uphill starting situation, thereby laying a reliable data foundation for subsequent target speed calculation and torque adjustment.

[0029] Through the implementation of step 101, by introducing multi-source sensor data to jointly collect information and combining it with the accelerator pedal opening signal, the current state of the vehicle and the driver's starting intention can be fully reflected, providing high-quality, multi-dimensional basic input for subsequent slope recognition and power distribution.

[0030] Step 102: Perform confidence fusion processing based on N types of sensor data to obtain real-time slope information;

[0031] In some examples, confidence fusion processing is a multi-source information fusion technology used to weight and integrate slope estimation data from different sensors according to their respective confidence weights, thereby improving the accuracy and robustness of the overall judgment. For example, if three sensors estimate the slopes as 4.9°, 5.1°, and 8.3°, respectively, 8.3° can be judged to have a large deviation and low confidence, and only a small weight, such as 0.1, is given. The other two are each given a higher weight, such as 0.45, resulting in a fusion result of approximately 5°. Real-time slope information refers to the angle between the current vehicle's driving surface and the horizontal plane. Uphill slopes are positive, and downhill slopes are negative. It is expressed in degrees (°) or slope percentage (%) and is used to dynamically reflect the terrain on which the vehicle is located.

[0032] For example, after receiving the slope data from the IMU, GPS and accelerometer, it was found that the three were 6.9°, 7.2° and 5.8°, respectively, with an average of about 6.6°; after calculating the deviation, it was found that the slope corresponding to the GPS was closer to the other two, so it was given a higher confidence level; finally, after weighted fusion, the real-time slope information output was 6.7°, which will be used to correct the power output, so that the vehicle will be smoother and more responsive when starting on a slope.

[0033] By implementing step 102 and performing confidence-weighted fusion on the data from multiple sensors, errors caused by individual sensor failures or noise interference can be effectively eliminated, and the accuracy and robustness of slope judgment can be improved, thereby providing more reliable slope information input for motor output control. This helps prevent the risk of slipping or insufficient output, especially in hill starting scenarios.

[0034] Step 103, determining a target speed of the target vehicle based on the accelerator pedal opening signal and the real-time slope information;

[0035] In some examples, the target speed refers to the reference speed value set for the motor during the launch process to meet the current slope road conditions and the driver's acceleration intention, used to guide the motor to output appropriate torque. The target speed is not the current actual speed, but the ideal operating state that the motor is expected to achieve, and can be used as a reference value for closed-loop control. The target speed is adjusted based on the degree to which the driver presses the accelerator pedal (reflecting the power request) and the current slope angle of the vehicle (reflecting the driving resistance or support requirements), thereby more accurately matching the driving intention with the launch environment. The base target speed can be found in the mapping curve between the target speed and the pedal opening preset in the vehicle. The slope compensation coefficient is then calculated based on the real-time slope. The product of the base target speed and the slope compensation coefficient is then used as the final target speed.

[0036] For example, the driver lightly presses the accelerator pedal (20%), which should output a lower target speed. However, since the real-time slope is +10°, the starting resistance is detected to be large. To prevent slipping, the target speed is automatically increased from 800rpm to 1080rpm, thereby driving the motor to output greater torque. Even if the pedal opening is small, it can meet the actual power demand of the current terrain and improve the stability and safety of starting.

[0037] By implementing step 103, combined with the driver's operating intention (accelerator pedal opening) and the actual driving environment (slope information), the target speed that meets the current working conditions can be dynamically set to achieve a precise match between power output and driving needs, thereby improving the responsiveness and smoothness of the start and avoiding discomfort or control failure caused by excessive or insufficient driving.

[0038] Step 104 , determining the motor output torque of the target vehicle based on the deviation between the target speed and the current speed;

[0039] In some examples, the current speed refers to the actual real-time speed of the electric vehicle's motor at start-up, which can be obtained using a speed sensor such as a rotary encoder or Hall effect sensor. For example, a current speed of 850 rpm indicates that the motor is currently rotating at 850 revolutions per minute. If the target speed is 1200 rpm and the current speed is 850 rpm, the deviation is +350 rpm. Motor output torque, measured in N·m, is the torque supplied to the wheels by the motor. Motor output torque directly affects vehicle acceleration, traction, and starting ability. The motor output torque is determined by the motor controller by adjusting the current output. The deviation can be calculated using a PI or PID control algorithm and converted into a desired torque output value. The controller achieves precise torque control using a drive current and back-EMF model. Determining the motor output torque based on the deviation between the target speed and the current speed is a closed-loop feedback control mechanism. When the actual speed is detected to be lower than the target speed, the motor torque output can be automatically increased to improve acceleration; otherwise, the output is reduced to maintain stability.

[0040] For example, during a hill start, the current motor speed is 850 rpm, significantly lower than the calculated target speed of 1200 rpm. A deviation of +350 rpm is immediately identified. To quickly improve power response and prevent hill roll, the PI controller adjusts the output current, increasing the motor torque to 180 N·m. This speeds up acceleration and quickly closes the gap between the target and actual speeds, ensuring an efficient and smooth start.

[0041] By implementing step 104, the output torque is adjusted in real time according to the deviation between the target speed and the current actual speed of the motor, which can improve the system response accuracy and effectively suppress output fluctuations, overshoot or hysteresis during the starting process, thereby ensuring the smoothness and energy efficiency of the starting process and reducing the impact load on the transmission system.

[0042] Step 105, controlling the target vehicle to travel with the motor output torque;

[0043] In some examples, based on the motor output torque value calculated in the previous step, the motor controller applies the torque to the wheels in real time, thereby driving the vehicle to achieve linear starting acceleration.

[0044] By implementing step 105, the vehicle start is controlled according to the finely adjusted motor torque output, thereby achieving higher starting stability, power responsiveness, and energy saving effects. This is particularly helpful in improving the vehicle start performance and ride comfort under complex operating conditions such as slopes, low adhesion, or high loads.

[0045] In summary, the embodiments of the present application can more accurately identify the current slope information of the vehicle by introducing multiple sensors and performing confidence fusion processing. Compared with the traditional method of relying on a single sensor, it has stronger anti-interference ability and environmental adaptability, can effectively improve the accuracy of slope perception, and provide more reliable input for subsequent control, especially when starting uphill, it can significantly reduce the risk of vehicle sliding downhill; the target speed is calculated based on the accelerator pedal opening and the real-time slope, so as to more accurately reflect the driver's starting needs and the current road conditions, and can achieve a high degree of matching between power output and driving intentions, thereby improving the smoothness and comfort of vehicle starting; by comparing the target speed with the current actual speed, the motor output torque is dynamically adjusted to achieve closed-loop fine control, which can effectively suppress speed fluctuations and energy waste during the starting process, and prevent starting jitter caused by torque overshoot or hysteresis, thereby significantly improving starting stability and energy efficiency performance. In summary, the vehicle starting control method provided in the embodiment of the present application improves the slope perception accuracy through multi-sensor fusion, combines pedal and slope information to achieve adaptive control of the target speed, and uses closed-loop torque regulation to optimize the starting process, thereby improving the safety, smoothness and energy efficiency of electric vehicle starting.

[0046] In some embodiments, the aforementioned step 102 may include: determining the mean slope information and N initial slope information based on N types of sensor data, wherein there is a one-to-one correspondence between the initial slope information and the sensor data; calculating the absolute deviation between each initial slope information and the mean slope information; when the absolute deviation of any initial slope information is less than or equal to a preset deviation threshold, updating the confidence weight corresponding to the initial slope information to a first preset weight; when the absolute deviation of any initial slope information is greater than the preset deviation threshold, updating the confidence weight corresponding to the initial slope information to a second preset weight, wherein the second preset weight is less than the first preset weight; based on the updated N confidence weights, performing weighted fusion on the N initial slope information to obtain real-time slope information.

[0047] In some examples, the mean slope information is a representative slope estimate obtained by averaging the initial slope information output by N sensors. The mean slope information can serve as a benchmark reference for determining whether other sensor values ​​deviate from the overall level. For example, assuming the initial slope information obtained from three sensors is 4.8%, 5.2%, and 5.0%, respectively, the mean slope information is (4.8 + 5.2 + 5.0) / 3 = 5.0%. The N initial slope information is independently calculated by N different types of sensors, with each sensor corresponding to a slope estimate. The absolute deviation is the absolute numerical difference between each initial slope information and the mean slope information. The preset deviation threshold is a tolerance limit used to determine whether the slope estimate deviates from the acceptable range. For example, it can be set to 0.3%. If it is less than or equal to this value, the estimate is reliable; otherwise, the reliability decreases. The first preset weight and the second preset weight are weights corresponding to "high confidence" and "low confidence", respectively, and can be floating-point numbers between 0 and 1. Each initial slope information can be multiplied by its corresponding confidence weight, and then weighted summed and normalized to obtain the final fused slope estimation value, that is, the real-time slope information.

[0048] For example, before the vehicle starts, the initial slope information provided by sensor A is 4.7%, the initial slope information provided by sensor B is 5.1%, and the initial slope information provided by sensor C is 5.0%. The calculated slope average information is 4.93%, and the deviation threshold is set to 0.3%. Since all deviations are within the threshold, a first weight (such as 0.33) is assigned accordingly, and then weighted fusion is performed to obtain the real-time slope information of 4.93%; if the interference deviation of a certain sensor is too large, its weight will be reduced, effectively suppressing the interference of abnormal data on the fusion result, ensuring that the slope estimation is stable and reliable, and then supporting subsequent precise control strategies such as speed regulation and torque compensation.

[0049] Through the implementation of the above embodiment, deviation analysis is performed on each initial slope information and the overall mean, and the confidence weight is dynamically adjusted. This can effectively eliminate abnormal sensor inputs and reduce the impact of single faults or transient interference on slope judgment, thereby improving the stability and accuracy of the fused slope information and further ensuring the reliability of the starting control logic.

[0050] In some embodiments, the aforementioned weighted fusion of N initial slope information based on the updated N confidence weights to obtain real-time slope information may include: obtaining resonance intensity data of the sensor corresponding to each initial slope information; determining N resonance correction coefficients corresponding to the N initial slope information based on the resonance intensity data, wherein the resonance correction coefficient is negatively correlated with the resonance intensity data; and weighted fusion of the N initial slope information based on the updated N confidence weights and the N resonance correction coefficients to obtain real-time slope information.

[0051] In some examples, resonance intensity data refers to the degree to which each sensor is affected by mechanical vibration during vehicle driving or specific operating conditions. This data can be obtained through signal spectrum analysis or the sensor's built-in vibration detection module. For example, the output signal of an IMU or accelerometer may exhibit high-frequency disturbances when subjected to strong road vibrations, which can affect the accuracy of slope determination. Resonance intensity data quantitatively reflects the degree to which a sensor is currently experiencing mechanical resonance or high-frequency vibration interference. Higher resonance intensity data indicates a greater likelihood of interference with the sensor signal and a lower confidence level in the slope data.

[0052] For example, when a vehicle starts on a bumpy road, the IMU sensor experiences high-frequency interference, with a resonance intensity coefficient as high as 0.9, and a corresponding resonance correction coefficient set to 0.4. The GNSS signal is stable, with a resonance intensity coefficient of only 0.2, and a corresponding resonance correction coefficient of 0.95. The resonance correction coefficient is then superimposed on the confidence weight model to suppress the unstable signal, thereby merging the generated slope information to more realistically reflect the actual road conditions and effectively improve the accuracy and robustness of speed and torque control when the vehicle starts.

[0053] Through the implementation of the above embodiments, the negative correlation correction mechanism of sensor resonance intensity is introduced on the basis of confidence fusion, which can further suppress the sensor input that is greatly affected by mechanical vibration, enhance the robustness of slope recognition to environmental changes (such as road vibration and vehicle body shaking), thereby improving the real-time and accuracy of target speed decision-making, and effectively preventing output abnormalities caused by misjudgment of slope.

[0054] In some embodiments, the aforementioned step 103 may include: determining a slope compensation coefficient based on real-time slope information; determining a target speed mapping curve based on the driving mode of the target vehicle; obtaining a basic target speed by querying the target speed mapping curve based on the accelerator pedal opening signal; and determining the target speed of the target vehicle based on the basic target speed and the slope compensation coefficient.

[0055] In some examples, the slope compensation factor is a correction parameter calculated based on real-time slope information to adjust the target speed, compensating for the impact of driving demand on uphill or downhill conditions. On an uphill slope, the speed should be increased to prevent a weak start, while on a downhill slope, the speed should be reduced to prevent the vehicle from sliding. The slope compensation factor can be obtained by looking up a table or function mapping based on the real-time slope angle. The driving mode reflects the user's driving preferences or scenario requirements. Different modes have different vehicle response characteristics, including Economy, Standard, and Sport. The driving mode directly influences the selection of the target speed mapping curve. For example, in Economy mode, which prioritizes energy conservation, the target speed response curve is relatively slow, while in Sport mode, the response is rapid and the target speed response curve is relatively steep. The target speed mapping curve describes the mapping relationship between accelerator pedal opening and target base speed. It is used to determine the motor's response based on the driver's intention. Different driving modes correspond to different target speed mapping curves. The base target speed is the speed value obtained from the target speed mapping curve based on the driver's acceleration intention (accelerator pedal opening), without considering the influence of conditions such as slope. It reflects the initial desired response.

[0056] For example, in sport mode, when the vehicle starts, a slope of +12° is detected, and the slope compensation coefficient is found to be 1.25; the accelerator pedal opening is 30%, and the basic target speed is found to be 1100 rpm on the corresponding target speed mapping curve; the basic target speed is multiplied by the slope compensation coefficient to calculate the final target speed of 1375 rpm; the result is finally fed back to the motor control unit to adjust the torque output, achieve enhanced power response, enable the vehicle to climb smoothly, and avoid slipping or stalling due to insufficient power.

[0057] Through the implementation of the above embodiment, the slope compensation and driving mode matching mechanism are used to dynamically adjust the calculation logic of the target speed to ensure that under different slopes and driving modes, the target speed can more reasonably reflect the power required by the vehicle, make the motor output more in line with the driver's intention, improve the power responsiveness and comfort of starting, and avoid setbacks or lags.

[0058] In some embodiments, the aforementioned vehicle starting control method may further include: obtaining the real-time slip rate of the tires of the aforementioned target vehicle; determining a slip rate correction coefficient based on the real-time slip rate of the tires of the target vehicle; the aforementioned determination of the target speed of the target vehicle based on the basic target speed and the slope compensation coefficient may include: multiplying the basic target speed, the slope compensation coefficient and the slip rate correction coefficient to obtain the target speed.

[0059] In some examples, tire slip is the difference between wheel speed and the actual vehicle speed, reflecting whether the wheel is slipping or spinning. For example, if the measured wheel speed corresponds to a linear velocity of 10 m / s and the vehicle speed sensor reads 9 m / s, the slip ratio is approximately (10–9) / 10 = 0.10. The slip correction factor is a scaling factor that adjusts the target speed based on the real-time slip ratio. It is used to suppress or mitigate the risk of loss of control during launch due to wheel slip. The correction factor is negatively correlated with the slip ratio; that is, the greater the tire slip ratio, the smaller the slip correction factor. The target speed is obtained by multiplying the base target speed, the slope compensation factor, and the slip correction factor. This factor can simultaneously account for driving intent, terrain requirements, and grip limitations to generate the final motor reference speed.

[0060] For example, when the vehicle starts on a slippery uphill road, the basic target speed of 900 rpm is first determined based on the pedal opening, and then adjusted to 1080 rpm through slope compensation; at this time, since the wheel detects a slip rate of 12%, the correction coefficient is found to be 0.85, and the final corrected target speed is 1080 rpm × 0.85 ≈ 918 rpm; this ensures sufficient driving force while avoiding excessive wheel idling, ensuring a safe and smooth start.

[0061] By implementing the above embodiment, the tire slip rate is introduced as a feedback parameter and the target speed is further corrected. It is possible to sense in real time whether the tire is slipping during the starting process, and to effectively suppress tire idling by adjusting the speed output, thereby improving starting grip and traction performance, adapting to low-adhesion roads such as rain, snow, gravel, and sand, and enhancing the terrain adaptability of the entire vehicle.

[0062] In some embodiments, the aforementioned determination of the slip rate correction coefficient based on the real-time slip rate of the tire of the target vehicle may include: when the real-time slip rate of the tire is less than a first slip threshold, determining the slip rate correction coefficient to be 1; when the real-time slip rate of the tire is greater than or equal to the first slip threshold and less than the second slip threshold, determining the slip rate correction coefficient to be a number less than 1 and greater than a preset coefficient, and the slip rate correction coefficient is negatively correlated with the real-time slip rate of the tire; when the real-time slip rate of the tire is greater than or equal to the second slip threshold, determining the slip rate correction coefficient to be a preset coefficient.

[0063] In some examples, the first slip threshold is a critical value used to distinguish between "no noticeable slip" and "slight slip" states. When the real-time tire slip rate is lower than the first slip threshold, it can be assumed that the wheel has good friction with the ground, and the slip rate correction factor is determined to be 1, without limiting the target speed. The second slip threshold is the upper limit that distinguishes "slight slip" from "severe slip" states. When the real-time tire slip rate reaches or exceeds the second slip threshold, maximum torque limiting must be applied to prevent continuous idling. The preset coefficient is the minimum scaling ratio of the target speed to the preset coefficient when the slip rate is very high, which is used to strictly limit the motor output to ensure that the wheels regain grip as quickly as possible.

[0064] For example, during a wet hill start test, the tire slip rates were detected as 0.03, 0.08, and 0.18, respectively. When the tire slip rate reached 0.03 (less than the first slip threshold of 0.05), the slip rate correction factor was set to 1, and no speed limit was applied. When the tire slip rate reached 0.08 (between the first slip threshold of 0.05 and the second slip threshold of 0.15), a slip rate correction factor of 0.9 was calculated using linear interpolation or a designed curve, and the target speed was moderately reduced to suppress slight slip. When the tire slip rate rose to 0.18 (greater than the second slip threshold of 0.15), the slip rate correction factor was immediately set to 0.7, maximizing the motor torque output, helping the tires quickly regain adhesion and ensuring a safe and smooth start. Through this segmented limiting and dynamic adjustment strategy, the vehicle can obtain the most appropriate power output at different slip stages, avoiding unnecessary excessive torque waste and effectively preventing severe slip and loss of control.

[0065] By implementing the above embodiment, correction coefficients are set for different slip ratio ranges, so that the system can moderately control the output during slight slip and quickly limit the torque during severe slip. This can effectively avoid the risk of aggravated slip or excessive control, improve the intelligent response capability of the method in critical states, and achieve high-safety and high-stability starting control.

[0066] In some embodiments, before step 105, the aforementioned vehicle starting control method may further include: extracting the peak points and trough points of the speed change curve of the target vehicle; when the number of trough points exceeds a preset number threshold and the values ​​of all trough points are less than a preset speed threshold, generating a starting shake determination signal; in response to the starting shake determination signal, reducing the motor output torque by a preset torque compensation value.

[0067] In some examples, a speed curve is a continuous graph of the actual motor or wheel speed over time during the vehicle's starting phase. Speed ​​(rpm) is plotted on the vertical axis against time on the horizontal axis, capturing the process of the motor starting and accelerating to a steady state. The motor controller can read the speed sensor data at a fixed sampling frequency (e.g., 100 Hz) and plot the curve. A peak is a local maximum point on the speed curve, i.e., a point higher than adjacent sampling points; a trough is a local minimum point on the speed curve, i.e., a point lower than adjacent sampling points. Peaks and troughs can reflect the frequency and amplitude of jitter during the start-up process. For example, detecting four troughs within one second indicates significant vehicle start-up jitter. A preset threshold is the minimum number of troughs required to determine the severity of the jitter. When the number of troughs exceeds this threshold, the vehicle is experiencing frequent start-up jitter and requires intervention. For example, the threshold can be set to three. The preset speed threshold is a lower speed limit used to identify excessive jitter amplitude. Only when the speed values ​​of all troughs are below this threshold is the system considered to be experiencing jitter due to output overshoot or hysteresis. The start-up jerk detection signal is a Boolean or event-based control signal. It is set to "true" when both the number of troughs and the speed conditions are met, informing the subsequent logic to perform jerk compensation. For example, after detecting four troughs below 500 rpm, the jerk detection signal is set to 1. The preset torque compensation value is the amount by which the motor torque should be temporarily reduced to eliminate jerk. For example, a torque compensation value of 20 N·m means that the motor output torque should be reduced by 20 N·m from the original value during jerk. When the jerk detection signal is true, the controller immediately subtracts the preset compensation value from the current torque command to reduce speed overshoot and suppress jerk.

[0068] Through the implementation of the above embodiment, the vibration characteristics of the motor speed change curve are identified, which can quickly determine whether there is starting jitter and timely trigger the torque reduction compensation measures, effectively alleviating the vehicle body jitter caused by factors such as overshoot and uncoordinated response, and can improve ride comfort and the smoothness of the motor system's operation, thereby extending the service life of the vehicle components.

[0069] Furthermore, as an implementation of the aforementioned method embodiment, the present application also provides a vehicle starting control device for implementing the aforementioned method embodiment. This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, the present vehicle starting control device embodiment will no longer describe the details of the aforementioned method embodiment one by one, but it should be clear that the device in the present application embodiment can implement all the contents of the aforementioned method embodiment. Figure 2As shown, the vehicle starting control device 20 includes: a data acquisition unit 201, a slope determination unit 202, a speed determination unit 203, a torque determination unit 204 and a vehicle starting unit 205, wherein the data acquisition unit 201 is used to obtain an accelerator pedal opening signal and N types of sensor data during the starting process of the target vehicle, wherein N is a natural number greater than 1; the slope determination unit 202 is used to perform confidence fusion processing based on the N types of sensor data to obtain real-time slope information; the speed determination unit 203 is used to determine the target speed of the target vehicle based on the accelerator pedal opening signal and the real-time slope information; the torque determination unit 204 is used to determine the motor output torque of the target vehicle based on the deviation value between the target speed and the current speed; and the vehicle starting unit 205 is used to control the target vehicle to travel with the motor output torque.

[0070] In some embodiments, the slope determination unit 202 is also used to determine the slope mean information and N initial slope information based on N types of sensor data, wherein there is a one-to-one correspondence between the initial slope information and the sensor data; calculate the absolute deviation between each initial slope information and the slope mean information; when the absolute deviation of any initial slope information is less than or equal to a preset deviation threshold, update the confidence weight corresponding to the initial slope information to a first preset weight; when the absolute deviation of any initial slope information is greater than the preset deviation threshold, update the confidence weight corresponding to the initial slope information to a second preset weight, wherein the second preset weight is less than the first preset weight; based on the updated N confidence weights, perform weighted fusion on the N initial slope information to obtain real-time slope information.

[0071] In some embodiments, the slope determination unit 202 is further used to obtain resonance intensity data of the sensor corresponding to each initial slope information; based on the resonance intensity data, determine N resonance correction coefficients corresponding to the N initial slope information, wherein the resonance correction coefficient is negatively correlated with the resonance intensity data; based on the updated N confidence weights and N resonance correction coefficients, perform weighted fusion on the N initial slope information to obtain real-time slope information.

[0072] In some embodiments, the speed determination unit 203 is also used to determine the slope compensation coefficient based on real-time slope information; determine the target speed mapping curve based on the driving mode of the target vehicle; obtain the basic target speed by querying the target speed mapping curve based on the accelerator pedal opening signal; and determine the target speed of the target vehicle based on the basic target speed and the slope compensation coefficient.

[0073] In some embodiments, the data acquisition unit 201 is also used to obtain the real-time slip rate of the tires of the target vehicle; based on the real-time slip rate of the tires of the target vehicle, a slip rate correction coefficient is determined; the speed determination unit 203 is also used to multiply the basic target speed, the slope compensation coefficient and the slip rate correction coefficient to obtain the target speed.

[0074] In some embodiments, the data acquisition unit 201 is also used to determine that the slip rate correction coefficient is 1 when the real-time slip rate of the tire is less than a first slip threshold; when the real-time slip rate of the tire is greater than or equal to the first slip threshold and less than the second slip threshold, determine the slip rate correction coefficient to be a number less than 1 and greater than a preset coefficient, and the slip rate correction coefficient is negatively correlated with the real-time slip rate of the tire; when the real-time slip rate of the tire is greater than or equal to the second slip threshold, determine the slip rate correction coefficient to be a preset coefficient.

[0075] In some embodiments, the vehicle starting control device 20 also includes a torque compensation unit for extracting peak points and trough points of the speed change curve of the target vehicle; when the number of trough points exceeds a preset number threshold and the values ​​of all trough points are less than a preset speed threshold, a starting jitter determination signal is generated; in response to the starting jitter determination signal, the motor output torque is reduced by a preset torque compensation value.

[0076] The present application also provides a computer-readable storage medium storing computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will be caused to execute any step of the vehicle starting control method provided in the present application.

[0077] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); or it may be various devices including one or any combination of the above memories.

[0078] In some embodiments, computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0079] In some embodiments, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (for example, files storing one or more modules, subroutines, or code portions).

[0080] In some embodiments, computer-executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0081] like Figure 3 As shown, the present application also provides an electronic device 30, including a memory 310, a processor 320 and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, any step of the above-mentioned vehicle starting control method is implemented.

[0082] The present application also provides a computer program product, comprising a computer program or computer-executable instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium and executes the computer program or computer-executable instructions, causing the electronic device to perform any step of the vehicle launch control method described above.

[0083] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle starting control method, characterized in that: include: Acquire an accelerator pedal opening signal and N types of sensor data during the start of the target vehicle, where N is a natural number greater than 1; Performing confidence fusion processing based on the N types of sensor data to obtain real-time slope information; determining a target speed of the target vehicle according to the accelerator pedal opening signal and the real-time slope information; Determining the motor output torque of the target vehicle based on a deviation between the target speed and the current speed; The target vehicle is controlled to travel using the motor output torque.

2. The vehicle starting control method according to claim 1, characterized in that: The confidence fusion processing based on the N types of sensor data to obtain real-time slope information includes: Determining, based on the N types of sensor data, mean slope information and N pieces of initial slope information, wherein there is a one-to-one correspondence between the initial slope information and the sensor data; Calculating the absolute deviation between each initial slope information and the slope mean information; When the absolute deviation of any initial slope information is less than or equal to the preset deviation threshold, the confidence weight corresponding to the initial slope information is updated to the first preset weight; When the absolute deviation of any initial slope information is greater than the preset deviation threshold, updating the confidence weight corresponding to the initial slope information to a second preset weight, wherein the second preset weight is smaller than the first preset weight; Based on the updated N confidence weights, the N initial slope information are weightedly fused to obtain the real-time slope information.

3. The vehicle starting control method according to claim 2, characterized in that: The step of performing weighted fusion on the N initial slope information based on the updated N confidence weights to obtain the real-time slope information includes: Obtaining resonance intensity data of the sensor corresponding to each initial slope information; Determining, based on the resonance intensity data, N resonance correction coefficients corresponding to the N initial slope information, wherein the resonance correction coefficients are negatively correlated with the resonance intensity data; Based on the updated N confidence weights and the N resonance correction coefficients, the N initial slope information is weightedly fused to obtain the real-time slope information.

4. The vehicle starting control method according to claim 1, characterized in that: The step of determining the target speed of the target vehicle according to the accelerator pedal opening signal and the real-time slope information includes: determining a slope compensation coefficient according to the real-time slope information; determining a target speed mapping curve based on the driving mode of the target vehicle; According to the accelerator pedal opening signal, querying the target speed mapping curve to obtain a basic target speed; The target speed of the target vehicle is determined according to the basic target speed and the slope compensation coefficient.

5. The vehicle starting control method according to claim 4, characterized in that: The vehicle starting control method further includes: Obtaining a real-time tire slip rate of the target vehicle; Determining a slip rate correction coefficient based on the real-time slip rate of the tire of the target vehicle; Determining the target speed of the target vehicle according to the basic target speed and the slope compensation coefficient includes: The target speed is obtained by multiplying the basic target speed, the slope compensation coefficient, and the slip ratio correction coefficient.

6. The vehicle starting control method according to claim 5, characterized in that: Determining the slip rate correction coefficient based on the real-time tire slip rate of the target vehicle includes: When the real-time tire slip rate is less than a first slip threshold, determining the slip rate correction coefficient to be 1; When the real-time tire slip rate is greater than or equal to the first slip threshold and less than a second slip threshold, determining the slip rate correction coefficient to be a number less than 1 and greater than a preset coefficient, and the slip rate correction coefficient is negatively correlated with the real-time tire slip rate; When the real-time tire slip rate is greater than or equal to the second slip threshold, the slip rate correction coefficient is determined to be the preset coefficient.

7. The vehicle starting control method according to claim 1, characterized in that: Before controlling the target vehicle to travel with the motor output torque, the vehicle starting control method further includes: Extracting peak points and trough points of a speed variation curve of the target vehicle; When the number of the trough points exceeds a preset number threshold and the values ​​of all the trough points are less than a preset speed threshold, a start-up jitter determination signal is generated; In response to the starting vibration determination signal, the motor output torque is reduced by a preset torque compensation value.

8. A vehicle starting control device, characterized in that: include: a data acquisition unit, configured to acquire an accelerator pedal opening signal and N types of sensor data during the starting process of the target vehicle, wherein N is a natural number greater than 1; a slope determination unit, configured to perform confidence fusion processing based on the N types of sensor data to obtain real-time slope information; a speed determination unit, configured to determine a target speed of the target vehicle according to the accelerator pedal opening signal and the real-time slope information; a torque determination unit, configured to determine an output torque of the motor of the target vehicle based on a deviation between the target speed and the current speed; A vehicle starting unit is used to control the target vehicle to travel using the torque output by the motor.

9. An electronic device comprising: A memory and a processor, wherein the processor is configured to implement the steps of the vehicle starting control method according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the vehicle launch control method according to any one of claims 1 to 7 are implemented.

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