Speed curve variable scooter speed control method

Abstract

The invention discloses a speed curve variable scooter speed control method which comprises the following steps: acquiring accelerator push amount and gear information, and calculating an expected target speed; acquiring the vehicle speed in real time, and determining an expected speed difference value; constructing a linear velocity path, and performing dynamic adjustment through the prediction model; according to the control precision and the response time, the linear speed path is uniformly divided; calculating an acceleration value according to the speed curve type; the actual speed of the scooter is monitored in real time, and the subsequent acceleration value is adjusted; and repeatedly executing the steps until the instruction is stopped. According to the method, the expected speed difference value is accurately calculated, the segmented speed paths are flexibly generated, and variable acceleration control on each segment of path is realized in combination with the acceleration curve variator, so that the change of the acceleration of the scooter can be smoothly adjusted in real time, the rapidness and stability of the driving process are ensured, and meanwhile, the driving comfort is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle speed control, and more specifically, particularly relates to a speed control method for a scooter with a variable speed curve. Background Art

[0002] Electric scooters are an important means of transportation in daily life, meeting people's daily travel needs, especially the elderly or those with limited mobility.

[0003] Like other transportation vehicles, electric scooters go through the process of acceleration and deceleration during driving. The appropriateness of the adjustment of this driving process will directly affect the driving experience. Traditional electric scooter manufacturers lack the adjustment of the driving experience of scooters. Most of them use fixed acceleration, which is easy to cause human discomfort and cannot flexibly provide suitable speed curves for different scooter models. Although some manufacturers use fixed variable acceleration to make the vehicle obtain an S-shaped speed curve within a fixed speed range, due to the fixed speed range and fixed speed curve, this type of implementation is not flexible enough and cannot meet the needs of all models or drivers.

[0004] However, there are some problems with the existing technology: in the existing technology, most traditional electric scooters use a fixed acceleration for driving. Since different drivers may have different sensitivity and acceptance of acceleration, the fixed acceleration may cause some drivers to have a series of physiological reactions when experiencing acceleration changes, such as adjustment of the vestibular system, changes in muscle tension, etc., thereby affecting driving comfort and safety. Therefore, we propose a speed control method for a scooter with a variable speed curve. Summary of the invention

[0005] In view of the problems existing in the prior art, the purpose of the present invention is to provide a speed control method for a mobility scooter with a variable speed curve. By accurately calculating the expected speed difference and flexibly generating a segmented speed path, combined with an acceleration curve changer, variable acceleration control on each path is achieved, so that the speed of the mobility scooter can be adjusted in real time and smoothly, ensuring that the driving process is both fast and stable, while improving the driving comfort and personalized experience.

[0006] To achieve the above object, the present invention provides the following technical solution: a speed control method for a mobility scooter with a variable speed curve, comprising the following steps:

[0007] S1, speed data collection: detect the throttle push amount and vehicle gear information, and calculate the desired target speed through the built-in algorithm;

[0008] S2, determine the expected speed difference: obtain the vehicle speed in real time, compare it with the target speed, calculate the speed difference between the two, and obtain the speed amount that needs to be adjusted;

[0009] S3, path planning and adjustment: Based on the expected speed difference, the acceleration and deceleration are estimated, a straight speed path from the current speed to the target speed is constructed, and the path is dynamically adjusted through the prediction model;

[0010] S4, according to the control accuracy and response time of the system, the number of sub-paths N is set, the straight speed path is evenly divided into N sub-paths, and real-time adjustment is performed;

[0011] S5, generating an acceleration curve: inputting the segmented speed path data into an acceleration curve converter, determining the acceleration variation law on the sub-speed path according to the speed curve type, and calculating the acceleration value;

[0012] S6, monitoring and feedback: monitor the actual speed of the scooter in real time through sensors, and automatically adjust the subsequent acceleration value according to the deviation size and direction;

[0013] S7, repeat and end: repeat the above steps until the scooter reaches the target speed or receives a stop command.

[0014] Optionally, the specific steps in step S1 include:

[0015] S11, detecting the push amount of the accelerator pedal in real time through the accelerator position sensor, converting the accelerator push amount signal into a numerical value, and performing filtering processing;

[0016] S12, detecting the current gear position of the vehicle through a gear position sensor;

[0017] S13, preliminarily estimating a basic speed increment according to the throttle push amount, and recording it as an expected speed change corresponding to the throttle push amount in the current gear;

[0018] S14, correcting the basic speed increment by adjusting the factor according to the gear position of the vehicle;

[0019] S15, superimposing the corrected basic speed increment with the current speed to obtain a preliminary expected target speed, and limiting the expected target speed to exceed the maximum speed limit of the vehicle.

[0020] Optionally, in step S2, the specific steps include:

[0021] S21, obtaining vehicle speed information in real time through a speed sensor;

[0022] S22, calculating the speed difference between the expected target speed and the current vehicle speed, and making an acceleration / deceleration determination based on the difference;

[0023] S23, performing absolute value processing on the speed difference to determine the speed adjustment amount;

[0024] The speed difference is calculated as follows:

[0025] Δv=v target -v current

[0026] Where Δv is the velocity difference;

[0027] v target is the target speed;

[0028] v current is the current speed.

[0029] Optionally, in step S3, the specific steps include:

[0030] S31, determine the acceleration limit to be a max , the deceleration limit is d max , the deceleration is calculated by taking the absolute value;

[0031] S32, acceleration / deceleration determination: when Δv>0, acceleration is required; when Δv<0, deceleration is required;

[0032] S33, estimated time required:

[0033] 3) Acceleration:

[0034] 4) Deceleration:

[0035] S34, constructing a straight line speed path and obtaining the corresponding speed at each time point t;

[0036] 1) During acceleration: v(t) = v current +a max t;

[0037] 2) When decelerating: v(t) = v current +d max t;

[0038] The prediction model dynamically adjusts the straight-line speed path from the current speed to the target speed based on the estimated value and the predicted speed change trend;

[0039] The linear speed path is a speed change trajectory.

[0040] Optionally, in step S4, the specific steps include:

[0041] S41, determine the control accuracy and response time: set the speed control error, determine the time t required for the system to receive the command and actually execute it response ;

[0042] S42, calculate the total time T, the calculation formula is:

[0043]

[0044] Where: v target is the target speed;

[0045] v current is the current speed;

[0046] a is the acceleration, if it decelerates, a is a negative value;

[0047] S43, setting the number of sub-paths N: Determine the number of sub-paths N according to the control accuracy and response time, and set the minimum sub-path time t min , the calculation formula of the sub-path number N is:

[0048]

[0049] S44, split speed path: evenly distribute the total speed change to N sub-paths:

[0050]

[0051] Where Δv i is the speed change of the i-th subpath;

[0052] S45, using the prediction model to estimate the speed change of the next sub-path within a certain time, and calculating and adjusting the acceleration or deceleration of the sub-path.

[0053] Optionally, in step S5, the specific steps include:

[0054] S51, determine the starting speed v of each subpath i , and the end velocity v i+1 , and determine the type of acceleration change;

[0055] S52, determine the variation law of the sinusoidal acceleration,

[0056] The acceleration calculation formula is:

[0057]

[0058] Where: A is the amplitude of acceleration;

[0059] φ is the phase shift;

[0060] S53, calculating the acceleration value at each time point on the path according to the sinusoidal acceleration variation law;

[0061] S54, applying the acceleration value to the vehicle control system to adjust the acceleration of the vehicle.

[0062] Optionally, the specific process of step S6 is:

[0063] S61, collects real-time data of the vehicle at a high frequency through sensors, and performs filtering and denoising on the data;

[0064] S62, compare the actual speed with the target speed, calculate the speed deviation and direction, dynamically adjust the acceleration according to the deviation size, direction and change trend, and introduce a prediction mechanism;

[0065] S63, adjusting the acceleration by controlling the output torque or current of the motor, continuously monitoring the actual speed change, and recording the effect of the acceleration adjustment;

[0066] S64 provides feedback based on the detected data changes, and dynamically adjusts the control parameters according to the feedback results and actual needs, using machine learning algorithms to optimize the control strategy.

[0067] Optionally, in step S64, the machine learning algorithm adopts a PID control algorithm;

[0068] The PID control algorithm sets up an anti-saturation mechanism, integral separation logic and differential first processing;

[0069] The anti-saturation mechanism is used to control the control amount output by the PID control module to prevent the output from exceeding the executable range when the physical limit of the throttle or brake system is reached. The specific process includes:

[0070] 1) Set the maximum and minimum limit values ​​u for throttle or brake max and u min ;

[0071] 2) Calculate the output u(t) of the PID control module,

[0072] 3) By comparing u(t), umax and umin, the corrected output is u corrected (t)=max(min(u(t)),u min );

[0073] The integral separation logic is used to reduce or eliminate the integral term when the error is small. The specific process is as follows:

[0074] 1) Determine the error threshold ε. If the error |e(t)| < ε, disable or reduce the integral term.

[0075] 2) The corrected PID output is:

[0076]

[0077] Among them: when |e(t)|<ε, β=[0,1);

[0078] When |e(t)|≥ε, β=1;

[0079] K p , K i , K d is the parameter of PID;

[0080] The step of differential first processing includes:

[0081] 1) Filter the error e(t) through a low-pass filter to obtain

[0082] 2) Yes Perform differential processing;

[0083] 3) The differential term in the corrected PID output is:

[0084] A speed control system for a mobility scooter with a variable speed curve, the system being used to implement the above method, comprising: a target speed acquisition module: comprising a throttle and a speed gear, for acquiring a target speed;

[0085] Microcontroller module: differential processor, path generator, acceleration curve changer, speed controller, receiving acceleration;

[0086] User interaction module: provides an interactive interface, including operations such as viewing the current status;

[0087] Fault diagnosis and safety module: monitors the health status and connection status of each sensor in the system, triggers alarms and takes safety measures when faults or abnormal conditions are detected;

[0088] Data recording and analysis module: records the operation data of the mobility scooter, including speed, acceleration, driving distance, etc.

[0089] Optionally, the target speed acquisition system acquires the driver's throttle push amount in real time through the vehicle throttle device and the vehicle gear device, and then calculates the required target speed in combination with the vehicle gear device;

[0090] In the difference processor, the microprocessor obtains the target speed, calculates the expected speed difference in combination with the current speed, and generates a linear speed path in combination with the set acceleration and deceleration;

[0091] The path generator receives a linear velocity path and reads an imported prediction model to generate a sub-path, then divides the velocity path into two equal parts according to a set number of sub-paths, and inputs the velocity path into the acceleration curve changer;

[0092] The acceleration curve changer receives and analyzes all sub-paths, receives the current speed fed back by the speed controller, and determines which path it is in based on the current speed, and then generates a new acceleration for the speed controller according to the current path;

[0093] The speed controller receives the acceleration output by the acceleration curve changer and sets a new speed according to the new acceleration.

[0094] The technical effects and advantages of the present invention are as follows: by accurately calculating the expected speed difference and flexibly generating a segmented speed path, the variable acceleration control on each segment of the path is realized by combining an acceleration curve changer, so that the acceleration of the mobility scooter can be adjusted in real time and smoothly, ensuring that the driving process is both fast and stable, while improving driving comfort;

[0095] Secondly, the prediction model is used to dynamically adjust the speed path, evenly divide the straight speed path into multiple sub-paths, and through real-time adjustment of the system, refined control of speed changes is achieved, and changes in acceleration are more accurately controlled, speed fluctuations and overshoot are reduced, and control accuracy and stability are improved. The acceleration value is calculated according to the speed curve type, and an acceleration curve that conforms to the vehicle's dynamic characteristics is generated to ensure the rationality and continuity of acceleration changes, further improving the stability and comfort of speed control.

[0096] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a framework diagram of the speed control method provided by the present invention;

[0098] Figure 2 It is a flow chart of monitoring and feedback steps provided by the present invention;

[0099] Figure 3 It is a flow chart of the speed control method provided by the present invention. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0101] like Figures 1 to 3 As shown, a speed control method for a mobility scooter with a variable speed curve provided by an embodiment of the present invention comprises the following steps:

[0102] S1, speed data collection: detect the throttle push amount and vehicle gear information, and calculate the desired target speed through the built-in algorithm;

[0103] S2, determine the expected speed difference: obtain the vehicle speed in real time, compare it with the target speed, calculate the speed difference between the two, and obtain the speed amount that needs to be adjusted;

[0104] S3, path planning and adjustment: Based on the expected speed difference, the acceleration and deceleration are estimated, a straight speed path from the current speed to the target speed is constructed, and the path is dynamically adjusted through the prediction model;

[0105] S4, according to the control accuracy and response time of the system, the number of sub-paths N is set, the straight speed path is evenly divided into N sub-paths, and real-time adjustment is performed;

[0106] S5, generating an acceleration curve: inputting the segmented speed path data into an acceleration curve converter, determining the acceleration variation law on the sub-speed path according to the speed curve type, and calculating the acceleration value;

[0107] S6, monitoring and feedback: monitor the actual speed of the scooter in real time through sensors, and automatically adjust the subsequent acceleration value according to the deviation size and direction;

[0108] S7, repeat and end: repeat the above steps until the scooter reaches the target speed or receives a stop command.

[0109] Furthermore, the prediction model is associated with the system, and through the road conditions provided by the system, including the congestion of the road ahead, the curvature of the curve, the slope change, etc., the speed path is adjusted in advance to avoid sudden braking or sudden acceleration, and improve the driving stability;

[0110] By dividing the speed path into multiple sub-paths, the system can more finely control the changes in acceleration and deceleration, thereby reducing the time required to transition from the current speed to the target speed. This fine control makes speed adjustments smoother and reduces unnecessary acceleration and deceleration processes. At the same time, it also means lower energy consumption and improves the endurance of the vehicle, which is especially important for new energy vehicles such as electric vehicles.

[0111] Specifically, the specific steps in step S1 include:

[0112] S11, detecting the push amount of the accelerator pedal in real time through the accelerator position sensor, converting the accelerator push amount signal into a numerical value, and performing filtering processing;

[0113] S12, detecting the current gear position of the vehicle through a gear position sensor;

[0114] S13, preliminarily estimating a basic speed increment according to the throttle push amount, and recording it as an expected speed change corresponding to the throttle push amount in the current gear;

[0115] S14, correcting the basic speed increment by adjusting the factor according to the gear position of the vehicle;

[0116] S15, superimposing the corrected basic speed increment with the current speed to obtain a preliminary expected target speed, and limiting the expected target speed to exceed the maximum speed limit of the vehicle.

[0117] Furthermore, filtering is a key step to remove signal noise and interference. During the acquisition process, the throttle thrust signal may be affected by various factors, such as electromagnetic interference, mechanical vibration, etc., resulting in signal fluctuations or distortion. Through filtering, the influence of these adverse factors can be effectively reduced, and the stability and reliability of the signal can be improved;

[0118] Furthermore, the adjustment factor is a parameter used to correct the basic speed increment according to the gear information, and its determination basis includes the vehicle's dynamic characteristics, the speed difference between gears, the driver's driving habits, etc. The value of the adjustment factor needs to be determined through experiments and calibration to ensure smooth and expected speed transition in different gears. In addition, as the vehicle's usage time increases and its performance changes, the value of the adjustment factor may also need to be adjusted accordingly;

[0119] Furthermore, after superimposing the corrected basic speed increment with the current speed, the desired target speed obtained is subject to the vehicle's maximum speed limit, thereby ensuring driving safety. By limiting the desired target speed to not exceed the vehicle's maximum speed limit, speeding and potential safety risks can be effectively prevented. At the same time, the speed limit also helps protect the vehicle's power system and transmission system from damage caused by excessive load.

[0120] Specifically, in step S2, the specific steps include:

[0121] S21, obtaining vehicle speed information in real time through a speed sensor;

[0122] S22, calculating the speed difference between the expected target speed and the current vehicle speed, and making an acceleration / deceleration determination based on the difference;

[0123] S23, performing absolute value processing on the speed difference to determine the speed adjustment amount;

[0124] The speed difference is calculated as follows:

[0125] Δv=v target -v current

[0126] Where Δv is the velocity difference;

[0127] v target is the target speed;

[0128] v current is the current speed.

[0129] Further, after obtaining the current vehicle speed and the desired target speed, the control system needs to calculate the difference between the two speeds to determine whether the vehicle should accelerate, decelerate, or maintain the current speed;

[0130] If the desired target speed is greater than the current speed, the difference is positive, indicating that acceleration is required;

[0131] If the desired target speed is less than the current speed, the difference is negative, indicating that deceleration is required;

[0132] If the two are equal, the difference is zero, indicating that no speed adjustment is necessary;

[0133] Based on the sign and size of the speed difference, the control system can make acceleration or deceleration decisions. For example, a threshold is set to determine whether acceleration or deceleration needs to be started, and the magnitude of acceleration or deceleration;

[0134] Furthermore, after determining whether acceleration or deceleration is required, the control system calculates the specific speed adjustment, that is, how long the vehicle needs to reach the desired target speed. Since the speed difference may be positive or negative, but the speed adjustment should be unsigned (i.e., it only indicates the size, not the direction), it is necessary to process the speed difference in absolute value. In this way, the same calculation method can be used to determine the speed adjustment whether it is acceleration or deceleration;

[0135] The control system calculates a reasonable speed adjustment amount based on the vehicle's acceleration performance, the difference between the current speed and the target speed, road conditions and other factors to ensure that the vehicle can reach the desired target speed smoothly and quickly. In actual applications, the speed adjustment amount may need to be dynamically adjusted according to the actual driving conditions of the vehicle.

[0136] For example, during acceleration, if the vehicle approaches the desired target speed, the control system can gradually reduce the acceleration to avoid overspeeding; during deceleration, if the vehicle has approached the desired target speed or emergency braking is required, the control system can quickly increase the braking force.

[0137] Specifically, in step S3, the specific steps include:

[0138] S31, determine the acceleration limit to be a max , the deceleration limit is d max , the deceleration is calculated by taking the absolute value;

[0139] S32, acceleration / deceleration determination: when Δv>0, acceleration is required; when Δv<0, deceleration is required;

[0140] S33, estimated time required:

[0141] 5) Acceleration:

[0142] 6) Deceleration:

[0143] S34, constructing a straight line speed path and obtaining the corresponding speed at each time point t;

[0144] 1) During acceleration: v(t) = v current +a max t;

[0145] 2) When decelerating: v(t) = v current +d max t;

[0146] The prediction model dynamically adjusts the straight-line speed path from the current speed to the target speed based on the estimated value and the predicted speed change trend;

[0147] The linear speed path is a speed change trajectory.

[0148] Furthermore, according to the current driving speed, the target speed and the estimated value, combined with the current driving state and the real-time road condition information, the predicted speed change trend is further corrected, the average acceleration required for a smooth transition from the current speed to the target speed is calculated, and then a preliminary speed path is generated that increases linearly from the current speed to the target speed;

[0149] Furthermore, during the driving process of the vehicle, the actual speed is continuously monitored and compared with the preliminary speed path. When the actual speed is lower than the path speed, the acceleration is increased; otherwise, the acceleration is reduced or even braking force is applied. In addition, the acceleration and deceleration limit values ​​are adjusted according to the changes in road conditions. For example, when going uphill, the acceleration may need to be increased to overcome the influence of gravity; when going downhill, the deceleration needs to be increased to control the speed. These are achieved based on the road information obtained by the prediction model.

[0150] It should be noted that during the adjustment process, a certain safety margin should always be retained to deal with emergencies or system errors.

[0151] Specifically, in step S4, the specific steps include:

[0152] S41, determine the control accuracy and response time: set the speed control error, determine the time t required for the system to receive the command and actually execute it response ;

[0153] S42, calculate the total time T, the calculation formula is:

[0154]

[0155] Where: v target is the target speed;

[0156] v current is the current speed;

[0157] a is the acceleration, if it decelerates, a is a negative value;

[0158] It should be noted that the above acceleration a is a constant value, and in actual application, the acceleration will be adjusted according to the road conditions and vehicle performance;

[0159] S43, setting the number of sub-paths N: Determine the number of sub-paths N according to the control accuracy and response time, and set the minimum sub-path time t min , the calculation formula of the sub-path number N is:

[0160]

[0161] S44, split speed path: evenly distribute the total speed change to N sub-paths:

[0162]

[0163] Where Δv i is the speed change of the i-th subpath;

[0164] S45, using the prediction model to estimate the speed change of the next sub-path within a certain time, and calculating and adjusting the acceleration or deceleration of the sub-path.

[0165] Furthermore, the prediction model is based on a data-driven machine learning model. By inputting the collected data into the model, combined with data such as changes in road conditions, changes in driver behavior, and sensor noise, the speed prediction value for a period of time is obtained, and based on the prediction results and the current speed, the starting speed and ending speed of the next sub-path are calculated. If the prediction results show that the speed will change (whether acceleration or deceleration), the required acceleration or deceleration is calculated based on the time and speed change of the sub-path.

[0166] Specifically, in step S5, the specific steps include:

[0167] S51, determine the starting speed v of each subpath i , and the end velocity v i+1 , and determine the type of acceleration change;

[0168] S52, determine the variation law of the sinusoidal acceleration,

[0169] The acceleration calculation formula is:

[0170]

[0171] Where: A is the amplitude of acceleration;

[0172] φ is the phase shift;

[0173] S53, calculating the acceleration value at each time point on the path according to the sinusoidal acceleration variation law;

[0174] S54, applying the acceleration value to the vehicle control system to adjust the acceleration of the vehicle.

[0175] Specifically, the specific process of step S6 is:

[0176] S61, collects real-time data of the vehicle at a high frequency through sensors, and performs filtering and denoising on the data;

[0177] S62, compare the actual speed with the target speed, calculate the speed deviation and direction, dynamically adjust the acceleration according to the deviation size, direction and change trend, and introduce a prediction mechanism;

[0178] S63, adjusting the acceleration by controlling the output torque or current of the motor, continuously monitoring the actual speed change, and recording the effect of the acceleration adjustment;

[0179] S64 provides feedback based on the detected data changes, and dynamically adjusts the control parameters according to the feedback results and actual needs, using machine learning algorithms to optimize the control strategy.

[0180] Furthermore, the real-time data of the vehicle is collected at a high frequency, filtered and de-noised to ensure the accuracy and reliability of the data. Subsequently, based on the deviation analysis between the actual speed and the target speed, the acceleration is adjusted dynamically, and a prediction mechanism is introduced to enhance the predictability of the control;

[0181] At the control execution level, the acceleration is precisely adjusted by precisely controlling the motor output, and the actual speed changes are continuously monitored to evaluate the control effect. Most importantly, this method uses machine learning algorithms to continuously optimize the control strategy so that it can adapt to different driving scenarios and needs, achieving more intelligent, efficient and safe vehicle control.

[0182] Specifically, in step S64, the machine learning algorithm adopts a PID control algorithm;

[0183] The PID control algorithm sets up an anti-saturation mechanism, integral separation logic and differential first processing;

[0184] The anti-saturation mechanism is used to control the control amount output by the PID control module to prevent the output from exceeding the executable range when the physical limit of the throttle or brake system is reached. The specific process includes:

[0185] 1) Set the maximum and minimum limit values ​​u for throttle or brake max and u min ;

[0186] 2) Calculate the output u(t) of the PID control module,

[0187] 3) By comparing u(t), umax and umin, the corrected output is u corrected (t)=max(min(u(t)),u min );

[0188] The integral separation logic is used to reduce or eliminate the integral term when the error is small. The specific process is as follows:

[0189] 1) Determine the error threshold ε. If the error |e(t)| < ε, disable or reduce the integral term.

[0190] 2) The corrected PID output is:

[0191]

[0192] Among them: when |e(t)|<ε, β=[0,1);

[0193] When |e(t)|≥ε, β=1;

[0194] K p , K i , K d is the parameter of PID;

[0195] The step of differential first processing includes:

[0196] 1) Filter the error e(t) through a low-pass filter to obtain

[0197] 2) Yes Perform differential processing;

[0198] 3) The differential term in the corrected PID output is:

[0199] Furthermore, the integral separation logic and differential look-ahead processing make the control process more flexible and efficient by intelligently adjusting the various components of the PID controller. The integral separation logic weakens the effect of the integral term when necessary to avoid unnecessary overshoot; while the differential look-ahead processing enables the system to adapt to changes more quickly by predicting and correcting in advance. This approach not only improves system performance, but also reduces energy consumption and wear, and extends the service life of the actuator;

[0200] The introduction of anti-saturation mechanism, integral separation logic and differential first processing PID control algorithm reduces the system's sensitivity to external interference and internal parameter changes. Even under complex and changeable working conditions, the system can maintain a stable control effect, reflecting a high degree of robustness.

[0201] A speed control system for a mobility scooter with a variable speed curve, the system and the method mentioned above, comprising: a target speed acquisition module: comprising a throttle and a speed gear, to acquire a target speed;

[0202] Microcontroller module: differential processor, path generator, acceleration curve changer, speed controller, receiving acceleration;

[0203] User interaction module: provides an interactive interface, including operations such as viewing the current status;

[0204] Fault diagnosis and safety module: monitors the health status and connection status of each sensor in the system, triggers alarms and takes safety measures when faults or abnormal conditions are detected;

[0205] Data recording and analysis module: records the operation data of the mobility scooter, including speed, acceleration, driving distance, etc.

[0206] Specifically, the target speed acquisition system acquires the throttle push amount of the driver in real time through the vehicle throttle device and the vehicle gear device, and then calculates the required target speed in combination with the vehicle gear device;

[0207] In the difference processor, the microprocessor obtains the target speed, calculates the expected speed difference in combination with the current speed, and generates a linear speed path in combination with the set acceleration and deceleration;

[0208] The path generator receives a linear velocity path and reads an imported prediction model to generate a sub-path, then divides the velocity path into two equal parts according to a set number of sub-paths, and inputs the velocity path into the acceleration curve changer;

[0209] The acceleration curve changer receives and analyzes all sub-paths, receives the current speed fed back by the speed controller, and determines which path it is in based on the current speed, and then generates a new acceleration for the speed controller according to the current path;

[0210] The speed controller receives the acceleration output by the acceleration curve changer and sets a new speed according to the new acceleration.

[0211] In summary, the present invention collects the throttle thrust and vehicle gear information in real time, calculates the expected target speed, ensures the accuracy of speed control, and calculates the difference between the expected speed and the actual speed, estimates the acceleration and deceleration according to the difference, constructs a straight-line speed path, realizes a smooth transition from the current speed to the target speed, avoids the discomfort caused by the sudden change of speed, and improves the driving comfort.

[0212] What is important is that the prediction model is used to dynamically adjust the speed path, to foresee and respond to possible external interference or internal changes, such as changes in road conditions and vehicle load, in advance, to enhance the adaptability and robustness of the system, and to ensure the stability and reliability of speed control. At the same time, the straight speed path is evenly divided into multiple sub-paths, and real-time adjustments are made based on the control accuracy and response time of the system to achieve refined control of speed changes, more accurately control changes in acceleration, reduce speed fluctuations and overshoot, and improve control accuracy and stability.

[0213] Furthermore, the acceleration change law on the sub-speed path is determined according to the speed curve type, and the acceleration value is calculated to generate an acceleration curve that conforms to the vehicle dynamics characteristics, thereby ensuring the rationality and continuity of the acceleration change, further improving the stability and comfort of speed control, and, in addition, monitoring the actual speed of the mobility scooter in real time through sensors, automatically adjusting subsequent acceleration values ​​according to the size and direction of the deviation, responding quickly to speed changes, and correcting deviations in a timely manner to ensure the real-time and accuracy of speed control.

[0214] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A speed control method for a mobility scooter with a variable speed curve, characterized in that: The steps include: S1, speed data collection: detect the throttle push amount and vehicle gear information, and calculate the desired target speed through the built-in algorithm; S2, determine the expected speed difference: obtain the vehicle speed in real time, compare it with the target speed, calculate the speed difference between the two, and obtain the speed amount that needs to be adjusted; S3, path planning and adjustment: Based on the expected speed difference, the acceleration and deceleration are estimated, a straight speed path from the current speed to the target speed is constructed, and the path is dynamically adjusted through the prediction model; S4, according to the control accuracy and response time of the system, the number of sub-paths N is set, the straight speed path is evenly divided into N sub-paths, and real-time adjustment is performed; S5, generating an acceleration curve: inputting the segmented speed path data into an acceleration curve converter, determining the acceleration variation law on the sub-speed path according to the speed curve type, and calculating the acceleration value; S6, monitoring and feedback: monitor the actual speed of the scooter in real time through sensors, and automatically adjust the subsequent acceleration value according to the deviation size and direction; S7, repeat and end: repeat the above steps until the scooter reaches the target speed or receives a stop command.

2. The method for controlling the speed of a mobility scooter with a variable speed curve according to claim 1, characterized in that: The specific steps in step S1 include: S11, detecting the push amount of the accelerator pedal in real time through the accelerator position sensor, converting the accelerator push amount signal into a numerical value, and performing filtering processing; S12, detecting the current gear position of the vehicle through a gear position sensor; S13, preliminarily estimating a basic speed increment according to the throttle push amount, and recording it as an expected speed change corresponding to the throttle push amount in the current gear; S14, correcting the basic speed increment by adjusting the factor according to the gear position of the vehicle; S15, superimposing the corrected basic speed increment with the current speed to obtain a preliminary expected target speed, and limiting the expected target speed to exceed the maximum speed limit of the vehicle.

3. The method for controlling the speed of a mobility scooter with a variable speed curve according to claim 1, characterized in that: In step S2, the specific steps include: S21, obtaining vehicle speed information in real time through a speed sensor; S22, calculating the speed difference between the expected target speed and the current vehicle speed, and making an acceleration / deceleration determination based on the difference; S23, performing absolute value processing on the speed difference to determine the speed adjustment amount; The speed difference is calculated as follows: Δv=v target -v current Where Δv is the velocity difference; v target is the target speed; v current is the current speed.

4. The method for controlling the speed of a mobility scooter with a variable speed curve according to claim 1, characterized in that: In step S3, the specific steps include: S31, determine the acceleration limit to be a max , the deceleration limit is d max , the deceleration is calculated by taking the absolute value; S32, acceleration / deceleration determination: when Δv>0, acceleration is required; when Δv<0, deceleration is required; S33, estimated time required: 1) Acceleration: 2) Deceleration: S34, constructing a straight line speed path and obtaining the corresponding speed at each time point t; 1) During acceleration: v(t) = v current +a max t; 2) When decelerating: v(t) = v current +d max t; The prediction model dynamically adjusts the straight-line speed path from the current speed to the target speed based on the estimated value and the predicted speed change trend; The linear speed path is a speed change trajectory.

5. The method for controlling the speed of a mobility scooter with a variable speed curve according to claim 1, characterized in that: Step S4 The specific steps include: S41, determine the control accuracy and response time: set the speed control error, determine the time t required for the system to receive the command and actually execute it response ; S42, calculate the total time T, the calculation formula is: Where: v target is the target speed; v current is the current speed; a is the acceleration, if it decelerates, a is a negative value; S43, setting the number of sub-paths N: Determine the number of sub-paths N according to the control accuracy and response time, and set the minimum sub-path time t min , the calculation formula of the sub-path number N is: S44, split speed path: evenly distribute the total speed change to N sub-paths: where Δv i is the velocity change of the i-th subpath; S45, using the prediction model to estimate the speed change of the next sub-path within a certain time, and calculating and adjusting the acceleration or deceleration of the sub-path.

6. The method for controlling the speed of a mobility scooter with a variable speed curve according to claim 1, characterized in that: In step S5, the specific steps include: S51, determine the starting speed v of each subpath i , and the end velocity v i+1 , and determine the type of acceleration change; S52, determine the variation law of the sinusoidal acceleration, The acceleration calculation formula is: Where: A is the amplitude of acceleration; φ is the phase shift; S53, calculating the acceleration value at each time point on the path according to the sinusoidal acceleration variation law; S54, applying the acceleration value to the vehicle control system to adjust the acceleration of the vehicle.

7. The method for controlling the speed of a mobility scooter with a variable speed curve according to claim 1, characterized in that: The specific process of step S6 is: S61, collects real-time data of the vehicle at a high frequency through sensors, and performs filtering and denoising on the data; S62, compare the actual speed with the target speed, calculate the speed deviation and direction, dynamically adjust the acceleration according to the deviation size, direction and change trend, and introduce a prediction mechanism; S63, adjusting the acceleration by controlling the output torque or current of the motor, continuously monitoring the actual speed change, and recording the effect of the acceleration adjustment; S64 provides feedback based on the detected data changes, and dynamically adjusts the control parameters according to the feedback results and actual needs, using machine learning algorithms to optimize the control strategy.

8. The method for controlling the speed of a mobility scooter with a variable speed curve according to claim 7, characterized in that: In step S64, the machine learning algorithm adopts a PID control algorithm; The PID control algorithm sets up an anti-saturation mechanism, integral separation logic and differential first processing; The anti-saturation mechanism is used to control the control amount output by the PID control module to prevent the output from exceeding the executable range when the physical limit of the throttle or brake system is reached. The specific process includes: 1) Set the maximum and minimum limit values ​​u for throttle or brake max and u min ; 2) Calculate the output u(t) of the PID control module, 3) By comparing u(t), u max and u min , the corrected output is u corrected (t)=max(min(u(t)),u min ); The integral separation logic is used to reduce or eliminate the integral term when the error is small. The specific process is as follows: 1) Determine the error threshold ε. If the error |e(t)| < ε, disable or reduce the integral term. 2) The corrected PID output is: Among them: when |e(t)|<ε, β=[0,1); When |e(t)|≥ε, β=1; K p , K i , K d is the parameter of PID; The step of differential first processing includes: 1) Filter the error e(t) through a low-pass filter to obtain 2) Yes Perform differential processing; 3) The differential term in the corrected PID output is:

9. A speed control system for a mobility scooter with a variable speed curve, characterized in that: The system is used to implement the method described in any one of claims 1 to 8, including: a target speed acquisition module: including a throttle and a speed gear, to acquire a target speed; Microcontroller module: differential processor, path generator, acceleration curve changer, speed controller, receiving acceleration; User interaction module: provides an interactive interface, including operations such as viewing the current status; Fault diagnosis and safety module: monitors the health status and connection status of each sensor in the system, triggers alarms and takes safety measures when faults or abnormal conditions are detected; Data recording and analysis module: records the operation data of the mobility scooter, including speed, acceleration, driving distance, etc.

10. The speed control system of a mobility scooter with a variable speed curve according to claim 9, characterized in that: The target speed acquisition system acquires the driver's throttle push amount in real time through the vehicle throttle device and the vehicle gear device, and then calculates the required target speed in combination with the vehicle gear device; In the difference processor, the microprocessor obtains the target speed, calculates the expected speed difference in combination with the current speed, and generates a linear speed path in combination with the set acceleration and deceleration; The path generator receives a linear velocity path and reads an imported prediction model to generate a sub-path, then divides the velocity path into two equal parts according to a set number of sub-paths, and inputs the velocity path into the acceleration curve changer; The acceleration curve changer receives and analyzes all sub-paths, receives the current speed fed back by the speed controller, and determines which path it is in based on the current speed, and then generates a new acceleration for the speed controller according to the current path; The speed controller receives the acceleration output by the acceleration curve changer and sets a new speed according to the new acceleration.

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