Novel electric bicycle and control method thereof

Abstract

The invention provides a novel electric bicycle and a control method thereof, relates to the technical field of electric vehicles, and aims to solve the technical problems of low intelligent degree and poor power output adaptability of a power assisting system of the electric bicycle in related technologies. The data acquisition unit is configured to acquire real-time riding data representing a current riding state, and the real-time riding data comprises rider operation data and vehicle posture data; a motor; a motor controller; the processor is connected with the plurality of sensors and the motor controller; the processor is configured to perform the following operations: determining a current rider intention state; generating a steering stability index representing a future short-time maneuvering trend; generating a terrain gradient index representing the gradient of the current road surface; determining a reference power assist coefficient; and generating a final motor power output instruction, and driving a motor through a motor controller.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of electric vehicles, and in particular to a new electric bicycle and a control method thereof. Background Art

[0002] Existing mainstream e-bike power-assist systems typically employ control strategies based on either pedal torque sensors or pedal frequency (or cadence) sensors. Torque-sensor-based systems align motor output power with the rider's pedal torque, providing a more natural and responsive riding experience. Frequency-sensor-based systems, on the other hand, determine the level of assistance based on pedal speed, offering a simpler structure and lower cost.

[0003] However, all traditional control strategies suffer from a fundamental limitation: they are all reactive, single-dimensional control models. Specifically, these systems determine power output based solely on the rider's instantaneous, single physical input (torque or frequency), completely ignoring the complex and dynamic context of the rider. Summary of the Invention

[0004] The embodiments of the present application provide a novel electric bicycle and a control method thereof, which are used to improve the technical problems in related technologies such as low intelligence level and poor adaptability of power output of the electric bicycle power assist system.

[0005] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0006] In the first aspect, the present application provides a new type of electric bicycle, comprising: multiple sensors configured to obtain real-time riding data representing the current riding status, the real-time riding data including rider operation data and vehicle posture data; a motor; a motor controller; a processor connected to the multiple sensors and the motor controller; the processor is configured to perform the following operations: determine the current rider intention state based on the pedal drive information in the rider operation data; generate a steering stability index representing the future short-term maneuvering trend based on the steering information in the vehicle posture data; generate a terrain gradient index representing the current road slope based on the pitch information in the vehicle posture data; based on the rider intention state, the steering stability index and the terrain gradient index, search and determine a baseline power assistance coefficient from a preset power adjustment strategy matrix; combine the baseline power assistance coefficient with the real-time drive request in the rider operation data to generate a final motor power output instruction, and drive the motor via the motor controller.

[0007] In a possible implementation of the first aspect, the multiple sensors include: a pedal torque sensor, a pedal frequency sensor, a brake sensor, a steering angle sensor and an inertial measurement unit; the rider operation data includes pedal torque, pedal frequency and brake signal; and the vehicle posture data includes pitch angle, roll angle and steering angle.

[0008] In a possible implementation of the first aspect, the processor is configured to determine the rider's intention state, specifically for: mapping the pedal drive information to one of a plurality of preset intention state categories based on a preset pedal torque threshold and a pedal frequency threshold, the intention state categories including starting state, cruising state, acceleration state, climbing state and coasting state.

[0009] In a possible implementation of the first aspect, when the processor is configured to generate the steering stability index, it is specifically used to: maintain a time series buffer that stores steering angle values ​​of multiple consecutive sampling points within a predetermined time window; obtain the degree of discreteness of the steering angle values ​​in the time series buffer; compare the degree of discreteness with a preset stability level threshold to classify the steering stability index into one of multiple discrete risk levels.

[0010] In a possible implementation of the first aspect, when the processor is configured to generate the terrain gradient index, it is specifically used to: compare the vehicle pitch angle indicated by the pitch information with multiple preset slope intervals; and determine that the terrain gradient index is one of uphill, flat road or downhill according to the slope interval to which the vehicle pitch angle belongs.

[0011] In a possible implementation of the first aspect, the power regulation strategy matrix is ​​a multidimensional lookup table stored in a memory connected to the processor, whose dimensions include various categories of the rider's intention state, various risk levels of the steering stability index, and various slope types of the terrain gradient index; each cell of the matrix stores a unique, preset baseline power assistance coefficient value.

[0012] In a possible implementation of the first aspect, the processor is further configured to: monitor whether the baseline power assistance coefficient remains constant within a preset time period; if the baseline power assistance coefficient remains constant within the preset time period, apply a preset micro-disturbance value to the currently determined baseline power assistance coefficient to generate a disturbed baseline power assistance coefficient, and use the disturbed baseline power assistance coefficient to generate the motor power output instruction.

[0013] In a possible implementation of the first aspect, the processor is further configured to: record the frequency of braking events occurring within a preset time window after applying high power assistance; if the frequency exceeds a preset intervention threshold, identify a global power inhibition signal; and in response to the global power inhibition signal, apply a uniform attenuation factor to all baseline power assistance coefficients determined from the power regulation strategy matrix within a subsequent predetermined adjustment period.

[0014] In a second aspect, the present application also provides a control method for an electric bicycle, comprising: obtaining real-time riding data representing a current riding state, the real-time riding data including rider operation data and vehicle posture data; determining a current rider intention state based on pedal drive information in the rider operation data; generating a steering stability index representing a future short-term maneuvering trend based on steering information in the vehicle posture data; generating a terrain gradient index representing a current road slope based on pitch information in the vehicle posture data; searching and determining a baseline power assistance coefficient from a preset power adjustment strategy matrix based on the rider intention state, the steering stability index, and the terrain gradient index; and generating a final motor power output instruction by combining the baseline power assistance coefficient with the real-time drive request in the rider operation data.

[0015] In a possible implementation of the first aspect, the step of generating a steering stability index includes: maintaining a time series buffer that stores steering angle values ​​of multiple consecutive sampling points within a predetermined time window; obtaining the degree of discreteness of the steering angle values ​​in the time series buffer; and comparing the degree of discreteness with a preset stability level threshold to classify the steering stability index into one of multiple discrete risk levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic structural diagram of a novel electric bicycle provided in some embodiments of the present application;

[0017] Figure 2 A flow chart of a control method provided for some embodiments of the present application;

[0018] Figure 3 for Figure 2 Flowchart of S300;

[0019] Figure 4 A flowchart of a control method provided for other embodiments of the present application;

[0020] Figure 5 A flowchart of a control method provided for some further embodiments of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0022] Hereinafter, the terms first, second, etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Thus, features defined as first, second, etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "a plurality" means two or more.

[0023] In addition, in this application, directional terms such as up, down, left, and right may be defined including but not limited to the orientation relative to the schematic placement of the components in the drawings. It should be understood that these directional terms may be relative concepts. They are used for relative descriptions and clarifications, and they may change accordingly according to changes in the orientation of the components in the drawings.

[0024] In this application, unless otherwise specified or limited, the term "connection" should be understood broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium. In addition, the term "electrical connection" can refer to the manner in which electrical connections are used to achieve signal transmission.

[0025] As used herein, about, approximately, or approximately includes the stated value and a reference value that is within an acceptable range of deviation from the particular value, characterized in that the acceptable range of deviation is as determined by one of ordinary skill in the art taking into account the measurements in question and the errors associated with the measurement of the particular quantity (i.e., the limitations of the measurement method).

[0026] In order to make the invention objectives, technical solutions and advantages of this application more clear, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0027] Existing mainstream e-bike power-assist systems typically employ control strategies based on either pedal torque sensors or pedal frequency (or cadence) sensors. Torque-sensor-based systems align motor output power with the rider's pedal torque, providing a more natural and responsive riding experience. Frequency-sensor-based systems, on the other hand, determine the level of assistance based on pedal speed, offering a simpler structure and lower cost.

[0028] However, all traditional control strategies suffer from a fundamental limitation: they are all reactive, single-dimensional control models. Specifically, these systems determine power output based solely on the rider's instantaneous, single physical input (torque or frequency), completely ignoring the complex, dynamic, and comprehensive context of the rider. For example:

[0029] When a rider prepares to enter a sharp turn, power assistance should ideally be proactively reduced for safety reasons. This is to prevent the risk of skidding caused by excessive speed or excessive power intervention when exiting the turn. However, traditional systems fail to recognize the intention to turn and continue to provide inappropriate power as long as the rider continues pedaling in the turn.

[0030] When encountering an uphill section, the torque-based system needs to wait until the rider has felt significant resistance and increased pedaling strength before increasing power assistance, resulting in a significant response lag. This results in a poor riding experience in the initial stage of the uphill ride.

[0031] Given the same torque input, the rider may be in different states of intent, such as starting from a standstill, cruising on a flat road, or attempting further acceleration at high speed. Traditional systems provide the same power response to the same torque input under these different states of intent. This clearly does not meet the requirements of intelligent and refined control and can lead to problems such as abrupt starts and uneven power during cruising.

[0032] Due to the inability to fully perceive the riding status, existing systems find it difficult to provide sufficient power while balancing safety in extreme situations and energy efficiency in daily riding.

[0033] Therefore, the electric bicycle power assist system in the existing technology generally has problems such as low intelligence, poor environmental adaptability, inability to deeply understand the rider's intentions, and the riding experience and safety need to be improved.

[0034] The embodiments of the present application provide a new electric bicycle and a control method thereof, which aim to solve the technical problems in the prior art that the power assist system of the electric bicycle cannot perceive the complex riding environment and the rider's deep intentions, resulting in a single power output response, low intelligence, and poor riding experience and safety.

[0035] The following will describe in detail the entire process of the electric bicycle and the control method executed within its system according to a preferred embodiment of the present application.

[0036] like Figure 1As shown, at the hardware level, the electric bicycle system includes conventional components such as a frame, wheels, battery, and motor. Its core control system includes a central processing unit (CPU), a connected memory, a set of coordinated sensors, and a motor controller. The sensor group is the foundation for the system's contextual awareness and, in this embodiment, includes a rider operation sensor and a vehicle posture sensor.

[0037] The rider operation sensor may include a pedal torque sensor mounted on the central axis for measuring the real-time torque value applied by the rider to the pedal A pedal frequency sensor (or calculated from the motor Hall sensor) to measure the rotation frequency of the pedal ; and a brake sensor installed at the brake handle, used to output a Boolean brake signal (For example, Indicates brake activation. stands for Inactive).

[0038] The vehicle attitude sensor includes an inertial measurement unit (IMU) installed near the geometric center of the frame. The IMU can output the vehicle's three-axis acceleration and three-axis angular velocity in real time, and calculate the vehicle's pitch angle through an internal fusion algorithm. and roll angle and a steering angle sensor mounted on the headset or front fork to measure the real-time steering angle of the handlebars .

[0039] The processor is responsible for executing the program instructions stored in the memory to implement the control method described in this application. The motor controller controls the speed and torque of the motor according to the final instructions generated by the processor.

[0040] The specific steps of the control method executed by the processor of the novel electric bicycle in the embodiment of the present application will be described in detail below. Figure 2 As shown, the method includes:

[0041] S100: Acquire real-time riding data representing a current riding state.

[0042] In this step, the processor runs at a preset high frequency (e.g. ) continuously collects raw data from the aforementioned sensor groups. This collected data forms the basis for all subsequent analysis and decision-making. During each sampling cycle, the processor acquires a data frame, which contains at least rider operation data and vehicle posture data.

[0043] The rider operation data may include current pedal torque , in Newton meters (Nm), the current pedal frequency , in revolutions per minute (RPM) and the current brake signal .

[0044] The vehicle posture data may include the current vehicle pitch angle , and the current steering angle .

[0045] The above data is sent to the processor's buffer in real time for processing in subsequent steps.

[0046] S200: Determine a current rider intention state based on pedal driving information in the rider operation data.

[0047] Conventional control methods usually use torque or cadence directly as input for power assistance, but this cannot distinguish whether the rider is leisurely cruising on flat ground or accelerating to overtake, even though the instantaneous torque of the two may be similar. and ) to discretize and classify the cyclist’s intention.

[0048] In this embodiment, five specific rider intention states are preset: : (Starting state), (cruise state), (Acceleration state), (climbing state) and (Sliding state).

[0049] The processor internally implements a set of deterministic classification rules based on multiple preset thresholds. For example, these rules can be designed as follows:

[0050] Define the threshold:

[0051] Torque threshold: Low torque threshold , medium torque threshold , high torque threshold .

[0052] Frequency Threshold: Low Frequency Threshold , mid-frequency threshold , high frequency threshold .

[0053] Vehicle speed threshold (obtained from motor or GPS): starting speed threshold .

[0054] Classification logic:

[0055] if or , then determine This means that the rider is exerting little power and the vehicle is coasting or about to stop.

[0056] if And the current speed , then determine This refers specifically to the phase of exerting force from a standstill or very low speed.

[0057] if and , then determine This means the cyclist is pedaling with greater force and frequency, intending to quickly increase speed.

[0058] if and , and at the same time in the subsequent step S400, it is determined that the terrain is uphill. This means the cyclist is working against the resistance of the slope with a continuous and steady force output.

[0059] After excluding all the above situations, for example and , then determine This means the cyclist is pedaling at a moderate, comfortable effort and pace, intending to maintain their current speed.

[0060] In this way, the system converts the continuous sensor signal into an unambiguous, discrete intent label. .

[0061] S300: Generate a steering stability index representing a future short-term maneuvering trend based on the steering information in the vehicle posture data.

[0062] Unlike traditional methods, this application does not rely on complex path planning or visual recognition. Instead, the processor analyzes the inherent dynamic characteristics of steering behavior to proactively assess the risk level of upcoming maneuvers (such as turns and obstacle avoidance). Understandably, the power assistance requirements for a smooth, gentle turn differ from those for a sharp, violent one. Over-powering a sharp turn can easily lead to skidding or loss of control.

[0063] To this end, this application constructs a steering stability index This method does not focus on the absolute value of the steering angle, but on its temporal variation pattern.

[0064] like Figure 3 As shown, the specific implementation process is as follows:

[0065] S310, maintain a time series buffer that stores the steering angle values ​​of multiple consecutive sampling points within a predetermined time window. A first-in-first-out (FIFO) buffer is set inside the processor to store the most recent Steering angle data of sampling points.

[0066] For example, assuming the sampling frequency is , users are concerned about the recent seconds of steering behavior, the buffer size At any time , the buffer Stored in .

[0067] S320: Obtain the discrete degree of the steering angle values ​​in the time series buffer.

[0068] The processor calculates the The variance or standard deviation of the steering angle values. Variance is a classic indicator of the degree of dispersion of data. A stable steering (such as straight driving or gentle cornering) will result in a buffer zone. If the value changes slightly, the variance will be small. On the contrary, a sharp turn, continuous S-turn or shaking of the handlebars will cause the variance to increase significantly.

[0069] Exemplarily, the processor calculates the variance ,in is the average value of the steering angle within the buffer zone.

[0070] S330: Compare the discrete degree with a preset stability level threshold to classify the steering stability index into one of a plurality of discrete risk levels.

[0071] Calculated variance value Will be used to determine the final steering stability index The indicator is divided into several discrete risk levels.

[0072] For example, three risk levels are preset: , , .

[0073] Defining Variance Thresholds: Low Risk Threshold , high risk threshold .

[0074] Hierarchical logic:

[0075] if ,but Indicates that the vehicle is traveling in a straight line or performing a very gentle maneuver and the risk is low.

[0076] if ,but Indicates that the vehicle is making a routine turn and there is a medium risk.

[0077] if ,but Indicates that the vehicle is making a sharp turn, making an emergency obstacle avoidance, or is in an unstable state, which is a high risk.

[0078] This metric also serves as a key discretized input into subsequent decision-making steps. It provides the system with an estimate of lateral stability, a dimension completely ignored by traditional e-bike control systems.

[0079] S400: Generate a terrain gradient index representing the current road slope based on the pitch information in the vehicle posture data.

[0080] The processor uses the vehicle pitch angle provided by the IMU , directly and in real time, sensing the vehicle's road gradient. Compared to retroactively determining uphill slopes by detecting motor load or increased rider torque, this attitude sensor-based approach is proactive. The pitch angle changes immediately upon entering a slope, allowing the system to anticipate the slope before the rider experiences any noticeable resistance.

[0081] The terrain gradient index The generation process is as follows:

[0082] S410: Compare the vehicle pitch angle indicated by the pitch information with a plurality of preset slope intervals.

[0083] The processor obtains the real-time pitch angle . Normally, when the frame is level , when going uphill , when going downhill .

[0084] For example, the following slope intervals are preset:

[0085] Flat road section:

[0086] Gentle uphill section:

[0087] Steep uphill section:

[0088] Gentle downhill section:

[0089] Steep downhill section:

[0090] S420: Determine, based on the slope interval to which the vehicle pitch angle belongs, that the terrain gradient index is one of uphill, flat road, or downhill.

[0091] The processor will Match the above intervals and output a discrete terrain gradient index .

[0092] For example, to simplify the decision dimension, multiple intervals can be merged:

[0093] if ,but (Uphill).

[0094] if ,but (Flat road).

[0095] if ,but (downhill).

[0096] this Indicators constitute the third dimension of the decision space, namely the vertical terrain dimension.

[0097] At this point, the system has refined the complex, continuous sensor data stream into three independent, discrete context labels: rider intention state , steering stability index , and terrain gradient indicators .

[0098] S500: Based on the rider's intention state, the steering stability index, and the terrain gradient index, search and determine a reference power assistance coefficient from a preset power adjustment strategy matrix.

[0099] The processor uses a look-up table mechanism, which is constructed as a power regulation strategy matrix in this application. The values ​​stored inside this matrix are (Baseline Power Assist Factor) is pre-set and calibrated based on extensive riding tests, safety engineering and ergonomic principles.

[0100] S510: Construct a power regulation strategy matrix.

[0101] The matrix is a three-dimensional matrix. Its three dimensions correspond to the three context labels generated in the previous steps.

[0102] Dimension 1 (row): rider intention state , including 5 levels (STARTUP, CRUISING, ACCELERATING, CLIMBING, COASTING).

[0103] Dimension 2 (column): Steering stability index , contains 3 levels (LOW, MEDIUM, HIGH).

[0104] Dimension 3 (page): Terrain Gradient Index , contains 3 levels (UPHILL, FLAT, DOWNHILL).

[0105] Therefore, the matrix contains a total of cells. Each cell Internal storage is between The floating point number between is the base power assist coefficient . Indicates that no power assistance is provided. Indicates that the maximum proportion of power assistance is provided.

[0106] For example, the setting concepts and values ​​of some cells of the matrix are described below:

[0107] : When the rider intends to climb a hill and the vehicle is traveling in a straight and stable manner on an uphill section, the system should provide a very high assistance coefficient to reduce the burden on the rider.

[0108] : When the rider intends to accelerate, but the vehicle is in a sharp turn or unstable state (high risk), the system must strictly limit the power assistance and reduce the coefficient to a very low level to ensure safety and prevent excessive power in the corner from causing loss of control.

[0109] : The most common riding state is to drive straight and steady with the intention of cruising on a flat road, providing a moderate and comfortable assistance coefficient.

[0110] :Starting on an uphill section is difficult, so when the intention is to start and the vehicle is still in a stable state, a higher assistance coefficient is provided to help the rider start smoothly.

[0111] : In any downhill situation, regardless of the rider's intention and steering state, the power assistance should be suppressed to an extremely low level (just to overcome the motor's own resistance), or even , to save energy and prevent unintentional acceleration on downhill slopes.

[0112] : When the intention is to coast, power assistance should not be provided under any circumstances.

[0113] S520: Find and determine a reference power assist coefficient.

[0114] In each control cycle, the processor performs a lookup operation. 、 and As an index, directly in Locate the corresponding cell in the matrix and read the value.

[0115] For example, if the current system status is:

[0116]

[0117]

[0118]

[0119] The processor will access the matrix Cell. Assume that the value stored in the cell is , then the reference power assistance coefficient of the current cycle is It was determined to be .

[0120] This matrix-based lookup method is extremely efficient and deterministic compared to complex real-time calculations, ensuring fast and consistent system response.

[0121] S600 : Generate a final motor power output instruction by combining the reference power assistance coefficient with the real-time driving request in the rider's operation data.

[0122] The processor will strategically Combined with the rider's current immediate drive request, the final control output is formed. The real-time drive request is usually directly derived from the pedal torque. .

[0123] Final motor power output command It can be generated by a combination function.

[0124] For example, the combined function can be a simple scaling function:

[0125]

[0126] in, is the maximum rated power of the motor, is the reference power assistance coefficient found in step S500, is the current real-time pedal torque, It is the reference maximum torque within the torque sensor's measuring range.

[0127] this The value is then sent to the motor controller, which drives the motor accordingly to provide the corresponding power.

[0128] For example, suppose , .

[0129] Scenario A: A cyclist accelerates hard on a straight road. The system recognizes , , . .but The system provides strong assistance.

[0130] Scenario B: Cyclists ride with the same torque into a sharp bend. The system recognizes , , . .but Although the rider is still exerting force, the system anticipates the risk and proactively reduces the power assistance significantly, ensuring cornering safety.

[0131] Through the above steps, the control method described in this application forms a complete closed loop, which not only responds to the rider's operation, but more importantly, can understand the intention behind the operation, predict the risks of maneuvering, perceive changes in terrain, and make the most intelligent power assistance decisions based on a comprehensive, expert knowledge-driven strategy library.

[0132] Under certain extremely monotonous riding conditions, such as riding at a constant speed for a long time on an absolutely flat and straight road, the input of the system ( , , ) may remain unchanged for a long time. This will cause the system to continuously output the exact same Although stable, this may result in a slightly mechanical riding experience and miss the opportunity to find a slightly better working point. To this end, this application proposes a dynamic coefficient micro-perturbation mechanism that runs in parallel outside the main process. Figure 4 As shown, the steps may include:

[0133] S710: Monitor whether the reference power assistance coefficient remains constant within a preset time period.

[0134] The processor maintains a timer inside. Whenever the This timer is reset when a change occurs. The timer continues to count up.

[0135] For example, a stagnation time threshold is set Second.

[0136] S720: If the reference power assistance coefficient remains constant within the preset time period, a preset perturbation value is applied to the currently determined reference power assistance coefficient.

[0137] Once the timer exceeds , the system thinks it has entered the state stagnation area. At this time, it will search for the Apply a small, preset perturbation value .

[0138] For example, Can be a The value may vary periodically (such as a sine wave) or be randomly selected.

[0139] In S600, the perturbed coefficient will be used To calculate .

[0140] This tiny disturbance breaks the absolute stability, and at the same time, it can help the system explore the energy efficiency performance near the current working point, providing a data basis for possible adaptive optimization. Once the riding state changes (such as the rider exerting force, turning), Once the timer is changed, the timer is reset, the perturbation mechanism is immediately suspended, and the main control logic takes over again.

[0141] The power regulation strategy matrix proposed in this application It is a preset, universally optimal strategy. However, different riders have different riding styles and preferences. An aggressive rider may find the system too protective, while a conservative rider may find it too aggressive in certain situations.

[0142] To this end, this application proposes a global inhibitory feedback mechanism based on braking patterns. The core idea of ​​this mechanism is that if the rider frequently brakes immediately after the system provides high power assistance, it usually means that the power provided by the system exceeds the rider's expectations or handling capabilities at the time, which is a negative feedback signal.

[0143] like Figure 5 As shown, the steps may include:

[0144] S810 : Record the frequency of braking events occurring within a preset time window after applying high power assistance.

[0145] The processor monitors two events:

[0146] Event A: generated in step S600 Exceeding a high power threshold (For example ).

[0147] Event B: Brake signal becomes .

[0148] The system will count a very short time window after event A occurs (For example The number of times event B occurs within a certain period of time (e.g., the past 10 minutes) is used to calculate the frequency.

[0149] S820: If the frequency exceeds a preset intervention threshold, a global power suppression signal is identified.

[0150] For example, an intervention threshold is set If the frequency of the above high-assisted emergency braking events exceeds this threshold, the processor generates a global power suppression signal. .

[0151] S830 : In response to the global power suppression signal, apply a unified attenuation factor to all reference power assistance coefficients determined from the power regulation strategy matrix in a subsequent predetermined regulation period.

[0152] once If activated, the system will adjust all the Apply a uniform attenuation factor .

[0153] For example, .

[0154] In S600, the coefficient actually used will become .

[0155] This means that the power response characteristics of the entire system will be softened by 10%. This is a macro-adaptive adjustment that fine-tunes the power strategy towards a more conservative and safer direction based on the rider's actual behavioral feedback, thereby achieving personalized adaptation to the individual riding style. After the adjustment cycle is completed, Can be restored to , the system restarts monitoring.

[0156] In summary, the method and system provided in the embodiments of the present application, by creatively constructing a multi-dimensional situational awareness framework and a matrix-based deterministic decision-making mechanism, supplemented by optional dynamic perturbations and adaptive feedback loops, can provide electric bicycles with an intelligent, personalized and highly safe power-assisted experience.

[0157] Those skilled in the art should understand that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0158] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0159] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0160] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units shown in this embodiment may be selected according to actual needs to achieve the purpose of this embodiment.

[0161] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware.

[0162] The above content is only a specific embodiment of this application, but the scope of protection of this application is not limited to this. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A new type of electric bicycle, characterized in that: include: A plurality of sensors are configured to obtain real-time riding data representing a current riding state, wherein the real-time riding data includes rider operation data and vehicle posture data; a motor; a motor controller; a processor connected to the plurality of sensors and the motor controller; The processor is configured to perform the following operations: determining a current rider intention state based on pedal actuation information in the rider operation data; generating a steering stability index representing a future short-term maneuvering trend based on steering information in the vehicle posture data; generating a terrain gradient index representing the current road slope based on the pitch information in the vehicle posture data; searching and determining a reference power assistance coefficient from a preset power adjustment strategy matrix based on the rider's intention state, the steering stability index, and the terrain gradient index; The reference power assist coefficient is combined with the real-time driving request in the rider's operation data to generate a final motor power output instruction, and the motor is driven via the motor controller.

2. The electric bicycle according to claim 1, characterized in that The multiple sensors include: a pedal torque sensor, a pedal frequency sensor, a brake sensor, a steering angle sensor and an inertial measurement unit; the rider operation data includes pedal torque, pedal frequency and brake signal; the vehicle posture data includes pitch angle, roll angle and steering angle.

3. The electric bicycle according to claim 1, characterized in that The processor is configured to determine the rider's intention state by: The pedal driving information is mapped to one of a plurality of preset intention state categories based on a preset pedal torque threshold and a pedal frequency threshold, wherein the intention state category includes a starting state, a cruising state, an accelerating state, a climbing state, and a coasting state.

4. The electric bicycle according to claim 1, characterized in that When the processor is configured to generate the steering stability index, it is specifically used to: Maintaining a time series buffer storing steering angle values ​​of a plurality of consecutive sampling points within a predetermined time window; Obtaining the discrete degree of the steering angle value in the time series buffer; The degree of dispersion is compared with a preset stability level threshold to classify the steering stability index into one of a plurality of discrete risk levels.

5. The electric bicycle according to claim 1, characterized in that When the processor is configured to generate the terrain gradient index, it is specifically used to: comparing the vehicle pitch angle indicated by the pitch information with a plurality of preset slope intervals; According to the slope interval to which the vehicle's nose-down angle belongs, the terrain gradient index is determined to be one of uphill, flat road or downhill.

6. The electric bicycle according to claim 1, characterized in that The power regulation strategy matrix is ​​a multidimensional lookup table stored in a memory connected to the processor, whose dimensions include various categories of the rider's intention state, various risk levels of the steering stability index, and various slope types of the terrain gradient index; each cell of the matrix stores a unique, preset baseline power assistance coefficient value.

7. The electric bicycle according to claim 1, characterized in that The processor is further configured to: monitoring whether the reference power assistance coefficient remains constant within a preset time period; If the baseline power assistance coefficient remains constant within the preset time period, a preset micro-disturbance value is applied to the currently determined baseline power assistance coefficient to generate a disturbed baseline power assistance coefficient, and the motor power output instruction is generated using the disturbed baseline power assistance coefficient.

8. The electric bicycle according to claim 1, characterized in that The processor is further configured to: Recording the frequency of braking events occurring within a preset time window after high power assistance is applied; If the frequency exceeds a preset intervention threshold, a global power suppression signal is identified; In response to the global power suppression signal, a uniform attenuation factor is applied to all reference power assistance coefficients determined from the power regulation strategy matrix in a subsequent predetermined regulation period.

9. A control method for an electric bicycle, characterized in that: include: Acquiring real-time riding data representing a current riding state, wherein the real-time riding data includes rider operation data and vehicle posture data; determining a current rider intention state based on pedal actuation information in the rider operation data; generating a steering stability index representing a future short-term maneuvering trend based on steering information in the vehicle posture data; generating a terrain gradient index representing the current road slope based on the pitch information in the vehicle posture data; searching and determining a reference power assistance coefficient from a preset power adjustment strategy matrix based on the rider's intention state, the steering stability index, and the terrain gradient index; The final motor power output instruction is generated by combining the reference power assistance coefficient with the real-time driving request in the rider operation data.

10. The method according to claim 9, characterized in that The step of generating the steering stability index comprises: Maintaining a time series buffer storing steering angle values ​​of a plurality of consecutive sampling points within a predetermined time window; Obtaining the discrete degree of the steering angle value in the time series buffer; The degree of dispersion is compared with a preset stability level threshold to classify the steering stability index into one of a plurality of discrete risk levels.

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