Electric two-wheeled vehicle control method and device

Abstract

This application provides a control method and apparatus for an electric two-wheeled vehicle. The method includes: determining the index value corresponding to each preset index among multiple preset indices in each set of vehicle performance parameters; classifying the index values ​​of the same preset index in the multiple sets of vehicle performance parameters into multiple preset index ranges; determining the target driving mode adopted by the electric two-wheeled vehicle from multiple driving modes based on the distribution of index values ​​within the multiple preset index ranges corresponding to each preset index in the multiple sets of vehicle performance parameters; and adjusting the control parameters of the electric two-wheeled vehicle according to the vehicle control strategy corresponding to the target driving mode. This application solves the problem of low control accuracy caused by coarse driving mode determination.

Description

Technical Field

[0001] This application relates to the field of electric two-wheeled vehicle control systems, and more specifically, to an electric two-wheeled vehicle control method and apparatus. Background Technology

[0002] In related technologies, the determination of driving modes in the electronic control systems of two-wheeled vehicles mainly relies on subjective judgment or simple parameter thresholds. This method of determining driving modes is overly simplistic and crude, leading to discrepancies between the determined driving style and actual driving behavior. This results in unreasonable adjustments to control strategies and fails to truly improve the driving experience. These shortcomings prevent existing methods of determining driving styles from accurately reflecting the driver's true driving habits, thus affecting the intelligent control effectiveness of electric two-wheeled vehicles. Summary of the Invention

[0003] This application provides a method and apparatus for controlling an electric two-wheeled vehicle, which at least solves the technical problem of low control accuracy caused by coarse driving mode determination in related technologies.

[0004] According to one aspect of the embodiments of this application, an electric two-wheeler control method is provided, comprising: when the electric two-wheeler is in operation, collecting multiple sets of vehicle performance parameters at a preset acquisition frequency; determining the index value corresponding to each preset index among multiple preset indicators in each set of vehicle performance parameters, and classifying the index values ​​of the same preset index in the multiple sets of vehicle performance parameters into multiple preset index ranges; determining the target driving mode adopted by the driving object of the electric two-wheeler from multiple driving modes according to the distribution state of the index values ​​in the multiple preset index ranges corresponding to each preset index in the multiple sets of vehicle performance parameters, and adjusting the control parameters of the electric two-wheeler according to the vehicle control strategy corresponding to the target driving mode; one driving mode in the multiple driving modes corresponds to one vehicle control strategy; one driving mode in the multiple driving modes is used to characterize a driving mode of the driving object.

[0005] According to another aspect of the embodiments of this application, an electric two-wheeler control device is also provided, comprising: a data acquisition module, configured to acquire multiple sets of vehicle performance parameters at a preset acquisition frequency during the riding of the electric two-wheeler; a statistics module, configured to determine the index value corresponding to multiple preset indicators in each set of vehicle performance parameters based on each set of vehicle performance parameters, and classify the index values ​​of the same preset indicator in the multiple sets of vehicle performance parameters into multiple preset index ranges; and a control module, configured to determine the target driving mode of the electric two-wheeler from multiple driving modes based on the distribution state of the index values ​​in the multiple preset index ranges corresponding to each preset indicator in the multiple sets of vehicle performance parameters, and adjust the control parameters of the electric two-wheeler according to the preset control strategy corresponding to the target driving mode.

[0006] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed by a processor.

[0007] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the steps in any of the method embodiments described above.

[0008] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0009] This application abandons the traditional driving mode switching method based on hard-line commands or static thresholds, and for the first time introduces a "multi-dimensional performance parameter distribution modeling + dynamic pattern recognition" mechanism into the electric control system of electric two-wheelers, realizing a paradigm upgrade from "human control mode" to "machine-recognized behavior". Traditional systems rely on the driver to manually select gears or rely on preset curves in the APP, and cannot perceive the real riding intention; while this solution collects multi-dimensional performance parameters such as throttle change rate, wheel speed difference, and bus current fluctuation at high frequency, and transforms the discrete sampled data into the statistical distribution form of each indicator within a preset range (such as bimodal distribution, high-entropy long tail, periodic oscillation, etc.), and uses this as the "statistical fingerprint" of the driving mode. This mechanism overcomes the limitations of single threshold discrimination, accurately identifying implicit behavioral patterns such as "aggressive" (high-frequency mutation + heavy-tailed distribution) and "energy-saving" (low variance + steady-state distribution). It effectively filters out transient disturbances and focuses on long-term behavioral characteristics, making control parameter adjustments no longer "mistriggered responses" but "habit-driven optimizations." This significantly improves the consistency between control strategies and user intentions, solving the technical problem of low control accuracy caused by the coarse judgment of driving modes in traditional systems. It establishes a complete closed-loop link in the two-wheeled vehicle field, encompassing "data acquisition—distributed modeling—pattern recognition—strategy adaptation," filling the gap in intelligent electronic control without cloud dependence and purely vehicle-side self-learning. This significantly improves the level of intelligence and user experience, and possesses high scalability and computational robustness. Attached Figure Description

[0010] Figure 1 This is a structural schematic diagram of an electric two-wheeled vehicle according to an embodiment of this application;

[0011] Figure 2 This is a flowchart illustrating an optional electric two-wheeled vehicle control method according to an embodiment of this application;

[0012] Figure 3 This is a schematic diagram of another optional electric two-wheeled vehicle control method according to an embodiment of this application;

[0013] Figure 4 This is a structural block diagram of an optional electric two-wheeled vehicle control device according to an embodiment of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] According to one aspect of the embodiments of this application, a control method for an electric two-wheeled vehicle is provided. Optionally, in this embodiment, the above-described control method for an electric two-wheeled vehicle may be applied, but is not limited to, to applications such as... Figure 1 Among the electric two-wheeled vehicles shown, such as Figure 1 As shown, the electric two-wheeler includes: a perception layer (radar / camera / millimeter wave / laser), a decision and control layer (VCU+TBOX (vehicle controller + remote communication terminal), IMU (inertial measurement unit), motor controller, status feedback and safety system (instrument, ABS (anti-lock braking system), BMS (battery management system), execution layer (motor, throttle), and communication bus (CAN / 485 / UART bus, cross-domain bus).

[0017] The system consists of several layers. The perception layer transmits environmental perception data to the VCU+TBOX via a cross-domain bus. The VCU+TBOX, as the core control unit of the vehicle, communicates with the IMU, instrument cluster, ABS, BMS, and motor controller via CAN / 485 / UART buses. It integrates perception data and system status information before issuing control commands. The IMU detects the vehicle's attitude and motion status and transmits it to the VCU+TBOX to assist in attitude stabilization control. The instrument cluster receives status data from the VCU+TBOX and provides the driver with information such as vehicle speed and battery level. The ABS prevents wheel lock-up during emergency braking and works with the VCU+TBOX to ensure braking safety. The BMS monitors battery status and transmits it to the VCU+TBOX to ensure battery safety and lifespan. The motor controller receives commands from the VCU+TBOX and, combined with throttle and brake signals, precisely controls the motor's power output. The motor, as the power source, drives the vehicle according to electronic control commands, ultimately achieving intelligent perception, decision-making, and power control for the electric two-wheeler.

[0018] The electric two-wheeled vehicle control method of this application embodiment can be executed by a motor controller. Figure 2 This is a schematic flowchart of an optional electric two-wheeled vehicle control method according to an embodiment of this application, as shown below. Figure 2 As shown, the process of this method may include the following steps:

[0019] Step S202: While the electric two-wheeler is in operation, collect multiple sets of vehicle performance parameters according to a preset collection frequency.

[0020] Step S204: Based on each set of vehicle performance parameters in the multiple sets of vehicle performance parameters, determine the index value corresponding to each preset index in the multiple preset indexes in each set of vehicle performance parameters, and classify the index values ​​of the same preset index in the multiple sets of vehicle performance parameters into multiple preset index ranges.

[0021] Step S206: Based on the distribution of index values ​​within multiple preset index ranges corresponding to each preset index in multiple sets of vehicle performance parameters, determine the target driving mode adopted by the electric two-wheeler from multiple driving modes, and adjust the control parameters of the electric two-wheeler according to the vehicle control strategy corresponding to the target driving mode; one driving mode in multiple driving modes corresponds to one vehicle control strategy; one driving mode in multiple driving modes is used to characterize a driving method of the driving object.

[0022] The electric two-wheeler control method in this embodiment can be applied to the field of electric two-wheeler control systems, and can be applied to scenarios such as daily riding and track driving.

[0023] Traditional electric two-wheeler control systems suffer from limitations in functionality and intelligence. They rely solely on manual adjustments of gear modes and acceleration curves via hard-wired signals from switches or app-based network protocols. They cannot record or analyze information such as rider habits, road conditions, and weather, hindering adaptive intelligent control and offering only a limited interaction method. Furthermore, the determination of driving modes in existing two-wheeler control systems primarily depends on subjective judgment or simple parameter thresholds. This overly simplistic and crude method leads to discrepancies between the determined driving style and actual driving behavior, resulting in unreasonable control strategy adjustments and failing to truly enhance the driving experience. These shortcomings prevent existing driving style determination methods from accurately reflecting the driver's true driving habits, thus impacting the effectiveness of intelligent control in electric two-wheelers.

[0024] To at least partially solve the above-mentioned technical problems, in this embodiment, multiple sets of vehicle performance parameters are collected in real time, the index values ​​are classified into multiple preset index ranges, and the target driving mode is determined based on the distribution state. This realizes intelligent driving style recognition based on actual riding data, which can adaptively match the driving mode without manual adjustment, thereby improving the system's intelligence and user experience.

[0025] Electric two-wheeled vehicles refer to two-wheeled vehicles powered by electricity, including but not limited to electric bicycles, electric motorcycles, and electric scooters. Their electronic control systems collect, process, and control various parameters of the vehicle through a microcontroller unit (MCU).

[0026] The preset sampling frequency refers to the parameter sampling interval set by the system, such as sampling once every 100ms. The motor controller collects vehicle performance parameters in real time through sensors (such as throttle sensors, IMU sensors, wheel speed sensors, etc.). The preset sampling frequency can be adjusted according to actual needs, for example, increasing the sampling frequency when driving at high speeds and decreasing the sampling frequency when driving at low speeds.

[0027] Multiple sets of vehicle performance parameters refer to multiple sets of physical quantity data reflecting the vehicle's operating status collected at a preset sampling frequency during the riding of an electric two-wheeler. Each set of vehicle performance parameters includes all vehicle performance parameters collected at the same time. The collection process of multiple sets of vehicle performance parameters is that during the riding of the electric two-wheeler, the motor controller collects data related to the driver's daily driving throttle habits in real time through sensors (such as throttle sensor, IMU sensor, wheel speed sensor, internal electronic control sensors, etc.) at a preset sampling frequency (e.g., every 100ms). For example, each set of vehicle performance parameters includes key information such as throttle opening (source: throttle), vehicle acceleration (source: IMU), vehicle speed (source: electronic control), motor torque (source: electronic control), front and rear wheel speeds (source: wheel speed sensor), bus voltage (source: electronic control), bus current (source: electronic control), phase current (source: electronic control), driving gear setting (source: VCU), TCS setting (source: VCU), and energy feedback (source: electronic control).

[0028] Preset indicators refer to key metrics extracted from vehicle performance parameters for analyzing driver habits. Examples of preset indicators include maximum throttle opening, maximum vehicle acceleration, maximum vehicle speed, maximum throttle opening change rate, maximum motor torque change rate, and maximum front-rear wheel speed difference. The indicator values ​​refer to the specific numerical values ​​of each preset indicator calculated based on the vehicle performance parameters.

[0029] In this embodiment, vehicle performance parameters are the raw time-series data collected (e.g., sampled every 100ms), while preset indicators are "feature quantities" obtained after statistical or differential operations on these raw data. For example: raw data "throttle opening" → preset indicator "maximum throttle opening"; raw data "throttle opening" + timestamp → preset indicator "throttle opening change rate"; the two have an upstream-downstream "data-feature" relationship, and the preset indicators are calculated based on the vehicle performance parameters. Vehicle performance parameters belong to "low-dimensional raw quantities," while preset indicators belong to "high-dimensional representation quantities." Vehicle performance parameters are refreshed once per sampling period; preset indicators are usually refreshed only once after an evaluation window (e.g., 30s) has ended.

[0030] Each preset indicator corresponds to multiple preset indicator ranges, which refer to multiple non-overlapping numerical intervals set for each preset indicator. These ranges are used to classify and statistically analyze indicator values, and the number of preset indicator ranges corresponding to each preset indicator is equal. For example, assuming multiple preset indicators include maximum throttle opening, maximum vehicle acceleration, and maximum vehicle speed, maximum throttle opening corresponds to 3 preset indicator ranges, maximum vehicle acceleration also corresponds to 3 preset indicator ranges, and similarly, maximum vehicle speed also corresponds to 3 preset indicator ranges.

[0031] The distribution of indicator values ​​within multiple preset indicator ranges corresponding to each preset indicator refers to the quantitative distribution of indicator values ​​within multiple preset indicator ranges for each preset indicator in multiple sets of vehicle performance parameters, reflecting the driving habits of the driver within different preset indicator ranges. By statistically analyzing the distribution characteristics of each indicator within its range, a multidimensional behavioral fingerprint is formed and matched with pre-stored driving patterns to determine the target driving mode of the current driver. The distribution of indicator values ​​within multiple preset indicator ranges corresponding to each preset indicator includes the frequency of occurrence of indicator values ​​within each preset indicator range (denoted as COUNT value), the concentration of indicator values ​​within each preset indicator range (measured by variance or standard deviation; smaller variance indicates greater concentration within a certain interval), the skewness of indicator values ​​within each preset indicator range, and the entropy of indicator values ​​within each preset indicator range (calculated by normalizing the COUNT of each interval to probability; smaller entropy indicates greater concentration, larger entropy indicates greater dispersion), etc.

[0032] Driving mode refers to the vehicle control mode determined based on the driver's driving habits, including but not limited to Master, Aggressive, Standard, and Eco modes. Different driving modes correspond to different vehicle control strategies. Target driving mode refers to the driving mode determined from multiple driving modes that matches the current driver's driving habits, based on the distribution of index values ​​within multiple preset index ranges corresponding to each preset index in multiple sets of vehicle performance parameters.

[0033] One driving mode corresponds to a set of combined statistical feature sets composed of the distribution states of the values of multiple preset indicators within their respective preset indicator ranges. For example, the combined statistical feature set corresponding to the energy-saving driving mode can be: throttle opening change rate: low variance, unimodal, mean < 20%; bus current fluctuation: Gaussian distribution, standard deviation < 5%, no long tail; wheel speed difference fluctuation: close to zero mean, kurtosis ≈ 0, no significant peak; energy recovery frequency: high probability of continuous triggering (distribution concentrated in the 70–100% interval).

[0034] Optionally, the target driving can be determined based on the distribution entropy of the values of the multiple preset indicators corresponding to each preset indicator. Specifically, set one driving style corresponding to one information entropy interval, and divide the driving styles into 5 levels according to the information entropy. The lower the entropy value, the more radical the style, and the higher the entropy value, the more conservative the style. Calculate the entropy value H for each preset indicator respectively; normalize the indicator count values (COUNT values) of each preset indicator within several preset indicator ranges (≥95%, ≥85%, ≥70%, ≥50%) to the probability distribution P=(p1,p2,p3,p4), and calculate its information entropy according to the following formula (1):

[0035] (1)

[0036] The smaller the entropy value, the more concentrated the indicator values in the high percentage interval, and the more radical the driving style; the larger the entropy value, the more uniform the distribution, and the more conservative the style. Take the weighted average Havg of all indicator entropy values, and the weights are given according to the importance of the indicators; if Havg ≤ 0.5, select the first driving style (the most radical); 0.5 < Havg ≤ 1.2, select the second driving style; 1.2 < Havg ≤ 1.8, select the third driving style; 1.8 < Havg ≤ 2.4, select the fourth driving style; Havg > 2.4, select the fifth driving style (the most conservative).

[0037] For example, within a 30s window, the COUNT distribution of the maximum throttle opening = [38,5,2,0], after normalization P = [0.84,0.11,0.04,0.00], H = 0.73; similarly, the maximum acceleration H = 0.69 and the torque change rate H = 0.78 are calculated; the weighted average Havg = 0.74 < 1.2, so the target driving mode is determined as "aggressive".

[0038] Optionally, the target driving mode can be determined based on the high-range percentage threshold of indicator values ​​within multiple preset indicator ranges corresponding to each preset indicator. Specifically, for each preset indicator, the occurrence ratio R of the "high range" is first calculated: R = (COUNT≥95% + COUNT≥85%) / total number of sampling periods; if within three consecutive windows, ≥70% of the preset indicators have an R≥60%, then the mode is directly upgraded to the corresponding aggressive mode; if within three consecutive windows, ≥70% of the preset indicators have an R≤15%, then the mode is downgraded to energy-saving mode; otherwise, the mode is maintained or adjusted step by step.

[0039] For example, in the last three 30-second windows, six out of eight preset indicators have an R≥60%, which meets the upgrade conditions. The controller will upgrade the target driving mode from "Sport" to "Master" and simultaneously adjust parameters such as bus current limiting and PWM output rate to 100%.

[0040] Optionally, the distribution of the COUNT value of each preset indicator within different preset indicator ranges can be analyzed, and the target driving mode can be determined based on the distribution characteristics of the COUNT value. Specifically, the motor controller first statistically analyzes the distribution of the COUNT value of each preset indicator across all preset indicator ranges, calculates the distribution characteristics of the COUNT value of each preset indicator across all preset indicator ranges, and determines the driving mode corresponding to each preset indicator based on the interval in which the distribution characteristics of the COUNT value of each preset indicator fall within all preset indicator ranges. A voting method is used to count the frequency of occurrence of each driving mode, and the driving mode with the highest frequency is selected as the target driving mode. If the frequency of occurrence is the same, a weighted method is used to set weight coefficients for different preset indicators, calculate the weighted score of each driving mode, and select the driving mode with the highest weighted score as the target driving mode.

[0041] For example, the motor controller first calculates the distribution of COUNT values ​​for each preset indicator across all preset indicator ranges. For instance, the COUNT values ​​for the maximum throttle opening within the four preset indicator ranges of ≥95%, ≥85%, ≥70%, and ≥50% are 40, 20, 10, and 5, respectively. Then, the controller calculates the distribution characteristics of the COUNT values, such as variance, standard deviation, skewness, and kurtosis. A smaller variance indicates that the COUNT values ​​are more concentrated within a specific preset indicator range; positive skewness indicates a long tail of COUNT values ​​in the higher preset indicator ranges; and higher kurtosis indicates that the COUNT values ​​are more concentrated within a specific preset indicator range. Next, the controller determines the driving mode corresponding to each preset index based on the distribution characteristics of the COUNT value. For example, when the variance of the COUNT value is <10, the skewness is >0.5, and the kurtosis is >3, it indicates that the COUNT value is concentrated in the high preset index range, and the driving mode corresponding to this preset index is Master; when the variance of the COUNT value is between 10 and 30 and the skewness is between 0.2 and 0.5, it indicates that the COUNT value is mainly distributed in the second-highest preset index range, and the driving mode corresponding to this preset index is Aggressive; when the variance of the COUNT value is between 30 and 60 and the skewness is between -0.2 and 0.2, it indicates that the COUNT value is evenly distributed across the preset index ranges, and the driving mode corresponding to this preset index is Sport; when the variance of the COUNT value is >60 and the skewness is <-0.5, it indicates that the COUNT value is concentrated in the low preset index range, and the driving mode corresponding to this preset index is Standard or Eco. Finally, the controller determines the target driving mode by integrating the driving modes corresponding to all preset indicators. It uses a voting method to count the occurrence frequency of each driving mode and selects the driving mode with the most occurrences as the target driving mode. If the occurrence frequencies are the same, a weighted method is used to set weight coefficients for different preset indicators. For example, the weight coefficient for maximum throttle opening is 0.2, the weight coefficient for maximum acceleration is 0.15, the weight coefficient for throttle opening change rate is 0.15, the weight coefficient for torque change rate is 0.1, and the weight coefficient for other preset indicators is 0.05. The weighted score of each driving mode is calculated, and the driving mode with the highest weighted score is selected as the target driving mode.

[0042] By analyzing the distribution of COUNT values ​​for each preset indicator across different preset indicator ranges, the distribution characteristics of driving styles can be comprehensively assessed, avoiding the limitations of a single COUNT value threshold. Determining the driving mode based on the distribution characteristics of COUNT values ​​can improve the accuracy and reliability of driving mode determination.

[0043] In this embodiment, the vehicle control strategy corresponding to each driving mode refers to the configuration scheme of control parameters corresponding to each driving mode. Control parameters are parameters used to adjust vehicle control performance; adjusting these parameters can change the vehicle's acceleration performance, handling performance, energy recovery efficiency, etc. For example, control parameters include bus current limiting, bus phase current, and pulse width modulation (PWM). The control parameters include the output frequency of the Pulse Width Modulation (PWM) system, hard start level, traction control system strength, traction control system sensitivity, energy recovery intensity, motor control carrier frequency, and adaptive cruise speed. Among these, bus current limiting refers to the maximum limit value of the bus current, used to protect the battery and motor from overcurrent damage; bus phase current refers to the limit values ​​of the bus and phase currents, used to control the motor's output power; pulse width modulation output frequency refers to the output frequency of the PWM signal, used to control the motor's speed and torque; hard start level refers to the acceleration level during vehicle startup, used to control the smoothness of vehicle startup; traction control system strength refers to the intervention intensity of the TCS system, used to control the power output of the vehicle under slippage conditions; traction control system sensitivity refers to the trigger sensitivity of the TCS system, used to control the triggering timing of the TCS system; energy recovery intensity refers to the energy recovery intensity during braking, used to control the energy recovery efficiency during braking; motor control carrier frequency refers to the carrier frequency of the motor control signal, used to control the smoothness of motor operation; and adaptive cruise speed refers to the cruising speed of the vehicle in adaptive cruise mode, used to control the vehicle's speed in cruise mode.

[0044] Optionally, during the riding of the electric two-wheeler, the motor controller collects vehicle performance parameters in real time through sensors (such as throttle sensor, IMU sensor, wheel speed sensor, internal electronic control sensors, etc.) at a preset acquisition frequency (e.g., every 100ms), obtaining multiple sets of vehicle performance parameters. Each set of parameters includes throttle opening, acceleration, vehicle speed, motor torque, front and rear wheel speeds, bus voltage, bus current, and phase current. Based on the collected multiple sets of vehicle performance parameters, the motor controller calculates the index value of each preset index, such as calculating the maximum throttle opening, throttle opening change rate, maximum acceleration, and acceleration change rate; then, it categorizes each index value according to the preset index range, counts the number of index values ​​(COUNT value) within each preset index range, and obtains the COUNT value for each preset index across multiple preset index ranges, such as the COUNT value for the maximum throttle opening within ≥95%, the COUNT value within ≥85%, etc. The motor controller analyzes the distribution of COUNT values ​​within multiple preset index ranges corresponding to each preset index. For example, when there are more COUNT values ​​in the high percentage range (e.g., ≥95%), a more aggressive driving mode is determined; when there are more COUNT values ​​in the low percentage range (e.g., ≥50%), a more conservative driving mode is determined. The target driving mode (e.g., Master, Intense, Sport, Standard, Eco) is determined, and the control parameters of the electric two-wheeler are adjusted according to the vehicle control strategy corresponding to that driving mode, such as adjusting bus current limiting, PWM output rate, and TCS intensity.

[0045] The embodiments provided in this application abandon the traditional driving mode switching method based on hard-line commands or static thresholds, and for the first time introduce a "multi-dimensional performance parameter distribution modeling + dynamic pattern recognition" mechanism into the electric control system of electric two-wheelers, realizing a paradigm upgrade from "human control mode" to "machine-recognized behavior". Traditional systems rely on the driver to manually select gears or rely on preset curves in the APP, and cannot perceive the real riding intention; while this solution collects multi-dimensional performance parameters such as throttle change rate, wheel speed difference, and bus current fluctuation at high frequency, and transforms the discrete sampled data into the statistical distribution form of each indicator within a preset range (such as bimodal distribution, high-entropy long tail, periodic oscillation, etc.), and uses this as the "statistical fingerprint" of the driving mode. This mechanism overcomes the limitations of single threshold discrimination, accurately identifying implicit behavioral patterns such as "aggressive" (high-frequency mutation + heavy-tailed distribution) and "energy-saving" (low variance + steady-state distribution). It effectively filters out transient disturbances and focuses on long-term behavioral characteristics, making control parameter adjustments no longer "mistriggered responses" but "habit-driven optimizations." This significantly improves the consistency between control strategies and user intentions, solving the technical problem of low control accuracy caused by the coarse judgment of driving modes in traditional systems. It establishes a complete closed-loop link in the two-wheeled vehicle field, encompassing "data acquisition—distributed modeling—pattern recognition—strategy adaptation," filling the gap in intelligent electronic control without cloud dependence and purely vehicle-side self-learning. This significantly improves the level of intelligence and user experience, and possesses high scalability and computational robustness.

[0046] In one exemplary embodiment, existing driving style determination methods lack a scientific classification of driving behavior and fail to distinguish between different types of performance indicators, leading to inaccurate driving style identification. To address this issue, this embodiment divides preset indicators into performance limit indicators and performance change rate indicators, and categorizes the values ​​of the same target performance limit indicator into multiple corresponding preset indicator ranges, and the values ​​of the same target performance change rate indicator into multiple corresponding preset indicator ranges. This achieves precise quantification of different types of driving behavior and improves the accuracy of driving style identification.

[0047] Among them, the number of preset index ranges corresponding to each preset index is equal; the multiple preset indexes include multiple performance limit indexes and multiple performance change rate indexes; the multiple performance limit indexes refer to the maximum performance value of the electric two-wheeler; the multiple performance change rate indexes refer to the rate of change of the vehicle performance of the electric two-wheeler.

[0048] In this embodiment, the vehicle-side MCU uses a model to periodically learn and calculate multiple sets of vehicle performance parameters (e.g., learning and update interval: every 10km, which can be freely set later) to form several preset indicators for relevant judgments. These preset indicators refer to specific parameters used to evaluate driving style, including multiple performance limit indicators and multiple performance change rate indicators. Performance limit indicators refer to the maximum values ​​(i.e., peak levels) of vehicle performance parameters, reflecting the extreme use of vehicle performance by the driver during riding. These include maximum throttle opening, maximum vehicle acceleration, maximum vehicle speed, maximum motor torque, maximum front and rear wheel speeds, maximum bus voltage, maximum bus current, and maximum phase current. Performance change rate indicators refer to the rate of change of vehicle performance parameters, reflecting the driver's control over changes in vehicle performance during riding. These include the rate of change of maximum throttle opening, the rate of change of maximum motor torque, the maximum difference between front and rear wheel speeds, the rate of change of maximum bus voltage, the rate of change of maximum bus current, and the rate of change of maximum phase current.

[0049] In some embodiments, classifying the index values ​​of the same preset index in multiple sets of vehicle performance parameters into multiple preset index ranges includes: classifying the index values ​​of the same target performance limit index in each set of vehicle performance parameters into multiple preset index ranges corresponding to the target performance limit index, and classifying the index values ​​of the same target performance change rate index in each set of vehicle performance parameters into multiple preset index ranges corresponding to the target performance change rate index.

[0050] Among them, the target performance limit index refers to the performance limit index that needs to be classified at present, such as maximum throttle opening and maximum acceleration. The target performance change rate index refers to the performance change rate index that needs to be classified at present, such as throttle opening change rate and torque change rate.

[0051] Optionally, the motor controller first categorizes multiple preset indicators into two types: performance limit indicators and performance change rate indicators. Then, for each set of vehicle performance parameters, the controller categorizes the values ​​of the same target performance limit indicator into corresponding preset indicator ranges. For example, it categorizes the maximum throttle opening into four preset indicator ranges: ≥95%, ≥85%, ≥70%, and ≥50%, and counts the number of indicator values ​​within each range. Simultaneously, the controller categorizes the values ​​of the same target performance change rate indicator into corresponding preset indicator ranges. For example, it categorizes the throttle opening change rate into four preset indicator ranges: ≥95%, ≥85%, ≥70%, and ≥50%, and counts the number of indicator values ​​within each range. Finally, the controller obtains the number of indicator values ​​within the multiple preset indicator ranges corresponding to each preset indicator for subsequent driving mode determination.

[0052] For example, Table 1 shows an example of categorizing the values ​​of the same preset index from multiple sets of vehicle performance parameters into multiple preset index ranges:

[0053] Table 1

[0054]

[0055] As shown in Table 1, the values ​​of the same preset index (such as the maximum throttle opening change rate) in multiple sets of vehicle performance parameters are categorized into four preset index ranges, and the number of index values ​​in each preset index range is counted. Other preset indexes in multiple sets of vehicle performance parameters are categorized into their corresponding multiple preset index ranges using relevant classification methods.

[0056] This embodiment achieves precise quantification of different types of driving behavior by dividing preset indicators into performance limit indicators and performance change rate indicators, and then classifying these two types of indicators separately. The performance limit indicator reflects the peak level of vehicle performance, while the performance change rate indicator reflects the drastic degree of change in vehicle performance. Combining the two allows for a comprehensive assessment of the driver's style. This addresses the problem of related technologies lacking a scientific classification of driving behavior in driving style determination methods, improving the accuracy and reliability of driving style recognition.

[0057] In one exemplary embodiment, existing driving mode determination methods lack a scientific division of indicator ranges. Overlapping preset indicator ranges lead to ambiguous determination results, and the lack of a clear correspondence between preset indicator ranges and driving modes results in inaccurate driving mode determination. To address this issue, this embodiment improves the accuracy and reliability of driving mode determination by setting multiple non-overlapping preset indicator ranges for each preset indicator, ensuring that each preset indicator's multiple preset indicator ranges correspond one-to-one with multiple driving modes, and assigning preset levels to each preset indicator's multiple preset indicator ranges.

[0058] As shown in Table 1, the multiple preset index ranges corresponding to each preset index do not overlap. For example, the four preset index ranges for the maximum throttle opening change rate in Table 1 are [95%, 100], (95%, 85%), (85%, 70%), and (70%, 50%). In this embodiment, the multiple preset index ranges corresponding to each preset index do not overlap, ensuring that each index value can only belong to one preset index range. This avoids the problem of ambiguity in the determination of index values ​​between multiple ranges and improves the accuracy of index classification.

[0059] In this embodiment, each preset indicator corresponds to multiple preset indicator ranges, which in turn correspond one-to-one with multiple driving modes. For example, the four preset indicator ranges for the maximum throttle opening change rate in Table 1 correspond to the four driving modes: Master, Sport, Standard, and Eco. The one-to-one correspondence between the multiple preset indicator ranges and multiple driving modes establishes a clear mapping relationship between the preset indicator ranges and driving modes, ensuring the uniqueness and traceability of the driving mode determination results.

[0060] In this embodiment, each preset indicator corresponds to multiple preset indicator ranges with preset levels, and different preset indicator ranges are assigned priorities, which facilitates graded judgment based on the aggressiveness of driving style and improves the flexibility of driving mode judgment.

[0061] In some embodiments, based on the distribution of index values ​​within multiple preset index ranges corresponding to each preset index in multiple sets of vehicle performance parameters, the target driving mode adopted by the driver of the electric two-wheeled vehicle is determined from multiple driving modes, including:

[0062] In response to the fact that the number of index values ​​within a specified preset index range corresponding to each preset index in multiple sets of vehicle performance parameters is greater than a preset number threshold, the target driving mode of the electric two-wheeler is determined to be the driving mode corresponding to the specified preset index range; the specified preset index range corresponding to each preset index refers to the index range of the specified preset level among multiple preset index ranges corresponding to each preset index.

[0063] Among them, the specified preset index range refers to the index range of the specified preset level among the multiple preset index ranges corresponding to each preset index. For example, for the maximum throttle opening index, ≥95% is the specified preset index range of the first level, and ≥85% is the specified preset index range of the second level.

[0064] The preset quantity threshold refers to the critical number of indicator values ​​used to determine the driving mode. When the number of indicator values ​​within a specified preset indicator range exceeds this threshold, the corresponding driving mode is determined. Different driving modes have different preset quantity thresholds.

[0065] Specifying a preset level refers to the level division of a preset indicator range. For example, the first level corresponds to the highest percentage range, the second level corresponds to the second highest percentage range, and so on.

[0066] Optionally, the motor controller first sets multiple non-overlapping preset indicator ranges for each preset indicator. For example, for the maximum throttle opening indicator, four non-overlapping preset indicator ranges are set: ≥95%, ≥85%, ≥70%, and ≥50%. Each preset indicator range corresponds to a driving mode, such as ≥95% for Master, ≥85% for Intense, ≥70% for Sport, and ≥50% for Standard. Then, the controller sets preset levels for each preset indicator range, such as ≥95% as Level 1, ≥85% as Level 2, ≥70% as Level 3, and ≥50% as Level 4. Next, the controller counts the number of indicator values ​​within the specified preset indicator range corresponding to each preset indicator, for example, counting the COUNT values ​​for maximum throttle opening within the ≥95% range. Finally, the controller compares the number of indicator values ​​within the specified preset indicator range corresponding to each preset indicator with a preset threshold. If the number exceeds the preset threshold, the target driving mode is determined to be the driving mode corresponding to the specified preset indicator range.

[0067] For example, during a 30-minute ride, the motor controller collected 18,000 sets of vehicle performance parameters, calculating indicators such as maximum throttle opening of 95%, throttle opening change rate of 60% / s, and maximum acceleration of 3m / s². The system categorized these indicators into corresponding preset ranges, obtaining a COUNT value of 250 for maximum throttle opening ≥95%, a COUNT value of 12,000 for throttle opening change rate ≥50%, and a COUNT value of 15 for maximum acceleration ≥70%. System analysis revealed that the COUNT value of 250 for maximum throttle opening ≥95% was greater than the preset threshold of 30, and that the COUNT values ​​for multiple performance limit indicators and performance change rate indicators within the ≥95% range were also greater than the preset threshold of 30. Therefore, the target driving mode was determined to be the driving mode corresponding to ≥95%, namely, the Master Driving Mode.

[0068] This embodiment achieves driving mode determination based on the number of indicator values ​​within a specified preset indicator range exceeding a preset threshold by setting multiple preset indicator ranges corresponding to non-overlapping preset indicator ranges, ensuring that each preset indicator range corresponds one-to-one with multiple driving modes, and assigning preset levels to each preset indicator range. The non-overlapping preset indicator ranges ensure that each indicator value belongs to only one preset indicator range, avoiding ambiguity in determining indicator values ​​across multiple ranges; the one-to-one mapping relationship ensures the uniqueness and traceability of the driving mode determination results; and the preset levels facilitate tiered determination based on the aggressiveness of driving styles. Responding to the situation where the number of indicator values ​​within a specified preset indicator range corresponding to each preset indicator in multiple sets of vehicle performance parameters exceeds a preset threshold, the target driving mode of the electric two-wheeled vehicle is determined to be the driving mode corresponding to the specified preset indicator range. This achieves driving mode determination based on objective statistical data, avoiding errors from subjective judgment. It solves the problem of the lack of scientific indicator range division and clear correspondence in related technologies for driving mode determination, improving the accuracy and reliability of driving mode determination.

[0069] In one exemplary embodiment, existing driving mode determination methods lack accurate identification of the most aggressive driving styles, failing to accurately identify driving behaviors that pursue ultimate performance, resulting in an inability to provide optimal control strategies in track driving or high-performance demand scenarios. To address this issue, this embodiment determines the target driving mode of the electric two-wheeled vehicle as the first driving mode by responding to a specified preset index range corresponding to each preset index as the first preset index range. This achieves accurate identification of the most aggressive driving styles, ensuring that optimal control strategies are provided in scenarios that pursue ultimate performance.

[0070] In some embodiments, the method further includes: in response to the specified preset index range corresponding to each preset index being a first preset index range, determining the target driving mode of the electric two-wheeled vehicle as a first driving mode; the first preset index range corresponding to each preset index refers to the index range with the largest boundary value among the multiple preset index ranges corresponding to each preset index; the first driving mode refers to the mode with the maximum power output and the lowest energy recovery efficiency.

[0071] The first preset indicator range refers to the indicator range with the largest boundary value among multiple preset indicator ranges corresponding to each preset indicator. The boundary value of the preset indicator range refers to the critical value of the preset indicator range, such as 95%, 85%, 70%, 50%, etc. The larger the boundary value, the more aggressive the driving style corresponding to that preset indicator range. For example, for the maximum throttle opening indicator, ≥95% is the first preset indicator range, ≥85% is the second preset indicator range, ≥70% is the third preset indicator range, and ≥50% is the fourth preset indicator range. The first preset indicator range corresponds to the highest percentage interval, representing the most aggressive driving behavior.

[0072] The first driving mode refers to the mode with the maximum power output and the lowest energy recovery efficiency. In this mode, the vehicle provides ultimate acceleration performance and handling, and is typically suitable for track driving or scenarios that pursue ultimate performance. Power output refers to the torque and power output of the electric motor in an electric two-wheeler, which is reflected in the vehicle's acceleration performance and top speed. The greater the power output, the stronger the acceleration performance and the higher the top speed, but the higher the energy consumption. Control parameters reflecting power output include bus current limiting, bus phase current, PWM output rate, hard start level, etc. The higher the values ​​of these parameters, the greater the power output.

[0073] Energy recovery efficiency refers to the efficiency with which an electric two-wheeler converts kinetic energy into electrical energy and stores it in the battery during deceleration or braking. It is reflected in the energy recovery intensity and the amount of energy recovered. Higher energy recovery efficiency results in a longer driving range, but may negatively impact the driving experience. Control parameters reflecting energy recovery efficiency include energy recovery intensity and motor control carrier frequency; the higher the values ​​of these parameters, the higher the energy recovery efficiency.

[0074] Figure 3 A flowchart of a control method for an electric two-wheeled vehicle provided in this application embodiment is shown below. Figure 3 As shown, the motor controller uses a customizable learning cycle of 10km to achieve the complete process of driving style recognition and mode matching: First, after the system completes the initial configuration, it enters the startup phase. The vehicle is powered on and running, connected via the APP, and the electric control switch is turned on. After disengaging the parking mode, riding begins at the user-preset initial gear. Then, the controller enters the data acquisition and processing phase, collecting real-time operating information such as throttle opening, motor torque, voltage, and current. After processing with moving average and Kalman filtering, two core feature indicators—the rate of change and extreme values ​​of driving behavior—are calculated. Next, the system determines the driving behavior and style based on these features. If the determination fails, it re-analyzes; if successful, it matches and outputs the corresponding determination mode, output mode, and target driving mode, such as large, aggressive, sport, standard, and energy-saving. Finally, all data from this cycle is recorded locally on the controller, and the number of acquisitions is accumulated. When 10km has been traveled, the process ends and automatically enters the next learning cycle, continuously optimizing driving style recognition and power output matching.

[0075] For example, the first driving mode can be either Master Driving Mode or Intense Driving Mode. The determination criteria and corresponding vehicle control strategies for Master Driving Mode and Intense Driving Mode are shown in Table 2 below:

[0076] Table 2

[0077]

[0078] The results for COUNT11-COUNT141 are shown in Table 1. In Table 2, N1-N7 represent bus current limit N1, bus phase current N2, PWM output rate N3, hard start level N4, TCS strength N5, TCS sensitivity N6, and energy recovery strength N7, respectively.

[0079] In this embodiment, when the values ​​of multiple preset indicators are all concentrated within the range of the first preset indicator, it indicates that the driver's driving behavior is very aggressive, frequently using the vehicle's highest performance. In this case, it is necessary to provide extreme power output and minimal energy recovery to meet the driver's performance demands. The first preset indicator range, as the highest percentage interval, can accurately identify the most aggressive driving style. The first driving mode, as the mode with the maximum power output and the lowest energy recovery efficiency, can provide the optimal control strategy for the most aggressive driving behavior, ensuring that the vehicle's performance is fully utilized.

[0080] Optionally, the motor controller first sets multiple preset index ranges for each preset index and determines the boundary values ​​corresponding to each preset index range. For example, for the maximum throttle opening index, four preset index ranges are set: ≥95%, ≥85%, ≥70%, and ≥50%, where ≥95% is the first preset index range, and the boundary value is 95%. Then, the controller counts the number of index values ​​within the specified preset index range corresponding to each preset index, for example, counting the COUNT values ​​for the maximum throttle opening within the ≥95% range. Next, the controller determines whether the specified preset index range corresponding to each preset index is within the first preset index range, i.e., whether the boundary value is the maximum value. Finally, when the specified preset index range corresponding to each preset index is within the first preset index range, the controller determines the target driving mode as the first driving mode and adjusts the control parameters according to the vehicle control strategy corresponding to the first driving mode.

[0081] For example, during a driving test, the motor controller collects vehicle performance parameters every 50ms, calculating values ​​such as maximum throttle opening of 100%, throttle opening change rate of 100% / s, maximum acceleration of 5m / s², and maximum torque of 100N·m. The system categorizes these values ​​into corresponding preset ranges, obtaining a COUNT value of 40 for maximum throttle opening ≥95%, 35 for throttle opening change rate ≥95%, 38 for maximum acceleration ≥95%, and 36 for maximum torque ≥95%. System analysis reveals that the specified preset ranges for maximum throttle opening, throttle opening change rate, maximum acceleration, and maximum torque are all within the first preset range (≥95%), and the COUNT value for each preset indicator within the first preset range is greater than the preset threshold of 30. Therefore, the system determines the target driving mode as the first driving mode, namely the master driving mode, and adjusts the control parameters as follows: bus current limit 100%, bus phase current 100%, PWM output rate 100%, hard start level 100%, TCS strength 0%, TCS sensitivity 0%, and energy recovery strength 0%.

[0082] This embodiment determines the target driving mode of an electric two-wheeler as the first driving mode by using a specified preset index range corresponding to each preset index as the first preset index range, thus achieving accurate identification of the most aggressive driving style. The first preset index range, as the highest percentage interval, accurately identifies driving behaviors where the driver frequently uses the vehicle's highest performance. The first driving mode, as the mode with the highest power output and lowest energy recovery efficiency, provides the optimal control strategy for the most aggressive driving behavior, ensuring that vehicle performance is fully utilized. By determining the target driving mode as the first driving mode based on the specified preset index range corresponding to each preset index, the problem of misjudging other driving modes in the most aggressive driving scenarios is avoided, ensuring the optimal control strategy is provided in scenarios pursuing ultimate performance. This solves the problem of related technologies' driving mode determination methods lacking accurate identification of the most aggressive driving style, improving the driving experience and vehicle performance in scenarios pursuing ultimate performance.

[0083] In one exemplary embodiment, existing driving mode determination methods lack accurate identification of moderately aggressive driving styles, failing to accurately identify driving behaviors that balance performance and range. This results in an inability to provide optimal control strategies in scenarios where a balance between performance and range is sought. To address this issue, this embodiment determines the target driving mode of the electric two-wheeled vehicle as the second driving mode by responding to the fact that the number of index values ​​within a second preset index range corresponding to each preset index in multiple sets of vehicle performance parameters exceeds a second quantity threshold. This achieves accurate identification of moderately aggressive driving styles, ensuring the provision of optimal control strategies in scenarios where a balance between performance and range is sought.

[0084] In some embodiments, the method further includes: in response to the number of index values ​​within a second preset index range corresponding to each preset index in a plurality of vehicle performance parameters being greater than a second quantity threshold, determining the target driving mode of the electric two-wheeled vehicle as a second driving mode; the second preset index range corresponding to each preset index refers to the index range with the largest boundary value among the plurality of preset index ranges corresponding to each preset index, excluding the first preset index range; the second driving mode refers to a mode that balances power output and energy recovery efficiency.

[0085] The second preset index range refers to the index range with the largest boundary value among the multiple preset index ranges corresponding to each preset index, excluding the first preset index range. For example, for the maximum throttle opening index, ≥95% is the first preset index range, ≥85% is the second preset index range, ≥70% is the third preset index range, and ≥50% is the fourth preset index range. The second preset index range corresponds to the second highest percentage range, representing moderately aggressive driving behavior.

[0086] The second driving mode refers to a mode that balances power output and energy recovery efficiency. In this mode, the vehicle provides high acceleration performance and handling while maintaining a certain range. It is typically suitable for everyday driving in good road conditions or scenarios where a balance between performance and range is sought. For example, the second driving mode could be a sport driving mode. The criteria for determining the sport driving mode and the corresponding vehicle control strategies are shown in Table 3 below:

[0087] Table 3

[0088]

[0089] The results for COUNT12-COUNT142 are shown in Table 1. In Table 3, N1-N7 represent bus current limit N1, bus phase current N2, PWM output rate N3, hard start level N4, TCS strength N5, TCS sensitivity N6, and energy recovery strength N7, respectively.

[0090] In this embodiment, when the values ​​of multiple preset indicators are all concentrated within the first preset indicator range, it indicates that the driver's driving behavior is very aggressive, frequently using the vehicle's highest performance. In this case, it is necessary to provide extreme power output and minimal energy recovery. However, in actual driving scenarios, not all drivers pursue extreme performance; many drivers' behavior falls between aggressive and conservative. If the judgment is based solely on the first preset indicator range, it may be misjudged as other driving modes. Therefore, this embodiment introduces a second preset indicator range and a second driving mode. When the values ​​of multiple preset indicators are concentrated within the second preset indicator range, it indicates that the driver's driving behavior is moderately aggressive, pursuing a certain level of performance while also considering range. In this case, a control strategy that balances power output and energy recovery efficiency is needed. The second preset indicator range, as the second highest percentage range, can accurately identify a moderately aggressive driving style. The second driving mode, as a mode that balances power output and energy recovery efficiency, can provide the optimal control strategy for moderately aggressive driving behavior, ensuring that vehicle performance and range are both taken into account.

[0091] Optionally, the motor controller first sets multiple preset index ranges for each preset index and determines the boundary values ​​corresponding to each preset index range. For example, for the maximum throttle opening index, four preset index ranges are set: ≥95%, ≥85%, ≥70%, and ≥50%, where ≥95% is the first preset index range, ≥85% is the second preset index range, ≥70% is the third preset index range, and ≥50% is the fourth preset index range. Then, the controller counts the number of index values ​​within the specified preset index range corresponding to each preset index, for example, counting the COUNT values ​​for the maximum throttle opening within the ≥85% range. Next, the controller determines whether the specified preset index range corresponding to each preset index is the second preset index range, that is, whether the boundary value is the maximum value other than the first preset index range. Finally, when the specified preset index range corresponding to each preset index is the second preset index range, and the number of index values ​​is greater than a second quantity threshold, the controller determines the target driving mode as the second driving mode and adjusts the control parameters according to the vehicle control strategy corresponding to the second driving mode.

[0092] For example, during a typical driving session with relatively good road conditions, the motor controller collects vehicle performance parameters every 100ms, calculating values ​​such as maximum throttle opening of 90%, throttle opening change rate of 80% / s, maximum acceleration of 4m / s², and maximum torque of 90N·m. The system categorizes these values ​​into corresponding preset ranges, obtaining a COUNT value of 35 for maximum throttle opening ≥85%, 30 for throttle opening change rate ≥85%, 38 for maximum acceleration ≥85%, and 32 for maximum torque ≥85%. System analysis reveals that the specified preset ranges for maximum throttle opening, throttle opening change rate, maximum acceleration, and maximum torque are all within the second preset range (≥85%), and the COUNT value for each preset indicator within the second preset range is greater than the second quantity threshold of 30. Therefore, the system determines the target driving mode as the second driving mode, namely the sport driving mode, and adjusts the control parameters as follows: bus current limit 90%, bus phase current 90%, PWM output rate 80%, hard start level 80%, TCS strength 50%, TCS sensitivity 50%, and energy recovery intensity 0%.

[0093] This embodiment determines the target driving mode of an electric two-wheeler as the second driving mode by responding to the fact that the number of index values ​​within the second preset index range corresponding to each preset index in multiple sets of vehicle performance parameters exceeds a second quantity threshold. This achieves accurate identification of moderately aggressive driving styles. The second preset index range, as the second-highest percentage interval, accurately identifies driving behaviors where the driver frequently uses the vehicle at higher but not highest performance levels. The second driving mode, as a mode balancing power output and energy recovery efficiency, provides the optimal control strategy for moderately aggressive driving behaviors, ensuring a balance between vehicle performance and range. Responding to the fact that the number of index values ​​within the second preset index range corresponding to each preset index exceeds the second quantity threshold, the target driving mode is determined as the second driving mode. This avoids misjudging other driving modes in moderately aggressive driving scenarios and ensures the provision of the optimal control strategy in scenarios seeking a balance between performance and range. This solves the problem of insufficient accurate identification of moderately aggressive driving styles in related technologies, improving the driving experience and vehicle performance in scenarios seeking a balance between performance and range.

[0094] In one exemplary embodiment, existing driving mode determination methods lack accurate identification of energy-saving driving styles and cannot accurately identify driving behaviors that prioritize energy recovery, resulting in an inability to provide optimal control strategies in scenarios where maximum range is sought. To address this issue, this embodiment determines the target driving mode of the electric two-wheeled vehicle as the third driving mode by responding to the fact that the number of index values ​​within a third preset index range corresponding to each preset index in multiple sets of vehicle performance parameters exceeds a third quantity threshold. This achieves accurate identification of energy-saving driving styles and ensures that optimal control strategies are provided in scenarios where maximum range is sought.

[0095] In some embodiments, the method further includes: in response to the number of index values ​​within a third preset index range corresponding to each preset index in a plurality of vehicle performance parameters being greater than a third quantity threshold, determining the target driving mode of the electric two-wheeled vehicle as a third driving mode; the third preset index range corresponding to each preset index refers to the index range with the largest boundary value among the plurality of preset index ranges corresponding to each preset index, excluding the first preset index range and the second preset index range; the third driving mode refers to the mode with the highest energy recovery efficiency.

[0096] The third preset indicator range for each preset indicator refers to the indicator range with the largest boundary value among the multiple preset indicator ranges corresponding to each preset indicator, excluding the first and second preset indicator ranges. For example, for the maximum throttle opening indicator, ≥95% is the first preset indicator range, ≥85% is the second preset indicator range, ≥70% is the third preset indicator range, and ≥50% is the fourth preset indicator range. The third preset indicator range corresponds to the low to medium percentage range, representing energy-saving driving behavior.

[0097] The third driving mode refers to the mode with the highest energy recovery efficiency. In this mode, the vehicle provides lower power output and maximum energy recovery intensity to maximize driving range. It is typically suitable for urban congestion, long-distance commuting, or scenarios where maximum range is desired. For example, the third driving mode can be an energy-saving mode. The criteria for determining the energy-saving mode and the corresponding vehicle control strategy are shown in Table 4 below:

[0098] Table 4

[0099]

[0100] The results for COUNT12-COUNT142 are shown in Table 1. In Table 4, N1-N7 represent bus current limit N1, bus phase current N2, PWM output rate N3, hard start level N4, TCS strength N5, TCS sensitivity N6, and energy recovery strength N7, respectively.

[0101] In this embodiment, when the values ​​of multiple preset indicators are concentrated within the range of the third preset indicator, it indicates that the driver's driving behavior is relatively conservative, using the vehicle's high performance less and focusing more on energy conservation and range. In this case, the maximum energy recovery efficiency and lower power output are required. In real-world driving scenarios, many drivers tend to prioritize energy conservation, especially in congested urban traffic or long-distance commutes, where they are more concerned with range than power performance. Therefore, this embodiment introduces a third preset indicator range and a third driving mode. When the values ​​of multiple preset indicators are concentrated within the third preset indicator range, it indicates that the driver's driving behavior is energy-efficient and focuses on energy recovery. In this case, a control strategy with the highest energy recovery efficiency is needed. The third preset indicator range, as a low to medium percentage range, can accurately identify energy-efficient driving styles, while the third driving mode, as the mode with the highest energy recovery efficiency, can provide the optimal control strategy for energy-efficient driving behavior, ensuring that the driving range is extended to the maximum extent.

[0102] Optionally, the specific implementation process of the motor controller performing the steps defined in claim 8 is as follows: First, the motor controller sets multiple preset index ranges for each preset index and determines the boundary values ​​corresponding to each preset index range. For example, for the maximum throttle opening index, four preset index ranges are set: ≥95%, ≥85%, ≥70%, and ≥50%, where ≥95% is the first preset index range, ≥85% is the second preset index range, ≥70% is the third preset index range, and ≥50% is the fourth preset index range. Then, the controller counts the number of index values ​​within the specified preset index range corresponding to each preset index, for example, counting the COUNT values ​​for the maximum throttle opening within the ≥70% range. Next, the controller determines whether the specified preset index range corresponding to each preset index is the third preset index range, that is, whether the boundary value is the maximum value other than the first and second preset index ranges. Finally, when the specified preset index range corresponding to each preset index is the third preset index range, and the number of index values ​​is greater than the third quantity threshold, the controller determines the target driving mode as the third driving mode and adjusts the control parameters according to the preset control strategy corresponding to the third driving mode.

[0103] For example, during a driving scenario in congested urban traffic, the motor controller collects vehicle performance parameters every 100ms, calculating values ​​such as maximum throttle opening of 75%, throttle opening change rate of 60% / s, maximum acceleration of 3m / s², and maximum torque of 75N·m. The system categorizes these values ​​into corresponding preset ranges, obtaining a COUNT value of 40 for maximum throttle opening ≥70%, 35 for throttle opening change rate ≥70%, 38 for maximum acceleration ≥70%, and 42 for maximum torque ≥70%. System analysis reveals that the specified preset ranges for maximum throttle opening, throttle opening change rate, maximum acceleration, and maximum torque all fall within the third preset range (≥70%), and the COUNT value for each preset indicator within the third preset range is greater than the third threshold value of 10. Therefore, the system determines the target driving mode as the third driving mode, namely the energy-saving driving mode, and adjusts the control parameters as follows: bus current limit 70%, bus phase current 70%, PWM output rate 65%, hard start level 60%, TCS strength 40%, TCS sensitivity 40%, and energy recovery intensity 50%.

[0104] This embodiment determines the target driving mode of an electric two-wheeler as the third driving mode by responding to the fact that the number of index values ​​within the third preset index range corresponding to each preset index in multiple sets of vehicle performance parameters exceeds a third threshold. This achieves accurate identification of energy-saving driving styles. The third preset index range, being a low to medium percentage range, accurately identifies driving behaviors where the driver uses the vehicle's high performance less frequently. The third driving mode, as the mode with the highest energy recovery efficiency, provides the optimal control strategy for energy-saving driving behaviors, ensuring maximum extension of the driving range. Responding to the fact that the number of index values ​​within the third preset index range corresponding to each preset index exceeds the third threshold, the target driving mode is determined as the third driving mode, avoiding misjudgment as other driving modes in energy-saving driving scenarios and ensuring the optimal control strategy is provided in scenarios where maximum range is pursued. This solves the problem of insufficient accurate identification of energy-saving driving styles in related technologies, improving driving range and driving experience in scenarios where maximum range is pursued.

[0105] In one exemplary embodiment, existing driving mode determination methods lack accurate identification of standard driving styles and cannot accurately identify driving behaviors that balance power output and energy recovery, resulting in an inability to provide optimal control strategies in daily commuting scenarios. To address this issue, this embodiment determines the target driving mode of the electric two-wheeled vehicle as the fourth driving mode by responding to the fact that the number of index values ​​within the fourth preset index range corresponding to each preset index in multiple sets of vehicle performance parameters exceeds a fourth quantity threshold. This achieves accurate identification of standard driving styles and ensures that optimal control strategies are provided in daily commuting scenarios.

[0106] In some embodiments, the method further includes: in response to the number of index values ​​within a fourth preset index range corresponding to each preset index in a plurality of vehicle performance parameters being greater than a fourth quantity threshold, determining the target driving mode of the electric two-wheeled vehicle as a fourth driving mode; the fourth preset index range corresponding to each preset index refers to the index range with the largest boundary value among the plurality of preset index ranges corresponding to each preset index, excluding the first preset index range, the second preset index range, and the third preset index range; the fourth driving mode refers to a mode in which the power output is greater than that of the third driving mode and the energy recovery efficiency is greater than that of the third driving mode.

[0107] The fourth preset indicator range refers to the indicator range with the largest boundary value among the multiple preset indicator ranges corresponding to each preset indicator, excluding the first, second, and third preset indicator ranges. For example, for the maximum throttle opening indicator, ≥95% is the first preset indicator range, ≥85% is the second preset indicator range, ≥70% is the third preset indicator range, and ≥50% is the fourth preset indicator range. The fourth preset indicator range corresponds to the low percentage range, representing standard driving behavior.

[0108] The fourth driving mode refers to a mode where the power output and energy recovery efficiency are both greater than those of the third driving mode. In this mode, the vehicle provides moderate power output and high energy recovery efficiency, ensuring basic power performance while maintaining range. It is typically suitable for standard driving scenarios such as daily commuting and urban roads. For example, the fourth driving mode can be a standard mode. The criteria for determining the standard mode and the corresponding vehicle control strategy can be shown in Table 5 below:

[0109] Table 5

[0110]

[0111] The results for COUNT12-COUNT142 are shown in Table 1. In Table 5, N1-N7 represent bus current limit N1, bus phase current N2, PWM output rate N3, hard start level N4, TCS strength N5, TCS sensitivity N6, and energy recovery strength N7, respectively.

[0112] The design rationale for determining the target driving mode of the electric two-wheeler as the fourth driving mode, based on the specified preset index range corresponding to each preset index, is that when the index values ​​of multiple preset indices are concentrated within the fourth preset index range, it indicates that the driver's driving behavior is relatively standard, neither excessively pursuing power performance nor excessively pursuing energy recovery, but maintaining a moderate driving style. In this case, a control strategy that provides moderate power output and energy recovery efficiency is needed. In actual driving scenarios, most drivers exhibit standard driving behavior, especially during daily commutes and urban road driving, where drivers need to strike a balance between power performance and range. Therefore, this embodiment introduces the fourth preset index range and the fourth driving mode. When the index values ​​of multiple preset indices are concentrated within the fourth preset index range, it indicates that the driver's driving behavior is standard, requiring both a certain level of power output and a certain level of energy recovery efficiency. In this case, a control strategy that provides moderate power output and energy recovery efficiency is needed. The fourth preset index range, as a low percentage range, can accurately identify the standard driving style. The fourth driving mode, as a mode with greater power output and energy recovery efficiency than the third driving mode, can provide the optimal control strategy for standard driving behavior, ensuring a balance between power performance and range.

[0113] Optionally, the specific implementation process of the motor controller performing the steps defined in claim 9 is as follows: First, the motor controller sets multiple preset index ranges for each preset index and determines the boundary values ​​corresponding to each preset index range. For example, for the maximum throttle opening index, four preset index ranges are set: ≥95%, ≥85%, ≥70%, and ≥50%, where ≥95% is the first preset index range, ≥85% is the second preset index range, ≥70% is the third preset index range, and ≥50% is the fourth preset index range. Then, the controller counts the number of index values ​​within the specified preset index range corresponding to each preset index, for example, counting the COUNT values ​​for the maximum throttle opening within the ≥50% range. Next, the controller determines whether the specified preset index range corresponding to each preset index is the fourth preset index range, that is, whether the boundary value is the maximum value other than the first, second, and third preset index ranges. Finally, when the specified preset index range corresponding to each preset index is the fourth preset index range, and the number of index values ​​is greater than the fourth quantity threshold, the controller determines the target driving mode as the fourth driving mode and adjusts the control parameters according to the preset control strategy corresponding to the fourth driving mode.

[0114] For example, during a daily commute, the motor controller collects vehicle performance parameters every 100ms, calculating values ​​such as maximum throttle opening of 65%, throttle opening change rate of 50% / s, maximum acceleration of 2.5m / s², and maximum torque of 65N·m. The system categorizes these values ​​into corresponding preset ranges, obtaining a COUNT value of 45 for maximum throttle opening ≥50%, 40 for throttle opening change rate ≥50%, 42 for maximum acceleration ≥50%, and 48 for maximum torque ≥50%. System analysis reveals that the specified preset ranges for maximum throttle opening, throttle opening change rate, maximum acceleration, and maximum torque are all within the fourth preset range (≥50%), and the COUNT value for each preset indicator within the fourth preset range is greater than the fourth threshold value of 10. Therefore, the system determines the target driving mode as the fourth driving mode, namely the standard driving mode, and adjusts the control parameters as follows: bus current limit 80%, bus phase current 80%, PWM output rate 75%, hard start level 70%, TCS strength 35%, TCS sensitivity 35%, and energy recovery strength 40%.

[0115] This embodiment determines the target driving mode of an electric two-wheeler as the fourth driving mode by responding to the fact that the number of index values ​​within the fourth preset index range corresponding to each preset index in multiple sets of vehicle performance parameters exceeds a fourth quantity threshold. This achieves accurate identification of standard driving style. The fourth preset index range, as a low percentage interval, accurately identifies driving behavior where the driver maintains a moderate driving style. The fourth driving mode, with higher power output and energy recovery efficiency than the third driving mode, provides the optimal control strategy for standard driving behavior, ensuring a balance between power performance and range. Responding to the fact that the number of index values ​​within the fourth preset index range corresponding to each preset index exceeds the fourth quantity threshold, the target driving mode is determined as the fourth driving mode, avoiding misjudgment as other driving modes in standard driving scenarios and ensuring optimal control strategy in daily commuting scenarios. This solves the problem of insufficient accurate identification of standard driving style in related technologies for driving mode determination methods, improving the driving experience and vehicle performance in daily commuting scenarios.

[0116] In one exemplary embodiment, existing control parameter adjustment methods lack precise matching with driving modes, failing to provide personalized control strategies based on different driving styles, resulting in a poor driving experience and underutilization of vehicle performance. To address this issue, this embodiment controls the electric two-wheeler's control parameters to specified values ​​according to the vehicle control strategy corresponding to the target driving mode. This achieves precise matching between control parameters and driving modes, providing personalized control strategies for drivers with different driving styles, thereby improving the driving experience and enhancing vehicle performance.

[0117] In some embodiments, the control parameters of the electric two-wheeler are controlled to reach specified parameter values ​​according to the vehicle control strategy corresponding to the target driving mode; the control parameters include at least one of the following parameters: bus current limiting, bus phase current, pulse width modulation output frequency, hard start level, traction control system strength, traction control system sensitivity, energy recovery strength, motor control carrier frequency, and adaptive cruise speed.

[0118] The specified parameter values ​​refer to the optimal control parameter configuration preset in the electronic control system corresponding to the target driving mode. These include bus current limiting, PWM output rate, hard start level, TCS intensity, energy recovery intensity, and motor carrier frequency. For example, when the target mode is "Master," the specified parameter values ​​are 100% bus current limiting, 0% TCS intensity, and 0% energy recovery to achieve ultimate power response; if it is "Energy Saving," the specified parameter values ​​are 100% TCS intensity, 100% energy recovery, and 60% PWM rate to optimize energy efficiency and smoothness. The specified parameter values ​​are derived by the system based on historical driving data training to ensure that the control strategy accurately matches the driving style.

[0119] Bus current limiting refers to limiting the maximum value of the bus current, used to protect the battery and motor from overcurrent damage; bus phase current limiting refers to limiting the maximum value of the phase current, used to protect the motor and controller from overcurrent damage; pulse width modulation output frequency refers to the output frequency of the PWM signal, used to control the motor's speed and torque output characteristics; hard start level refers to the current and torque output characteristics of the motor during startup, used to control the smoothness and response speed of startup; traction control system strength refers to the degree of intervention of the traction control system, used to control the intervention force when wheels slip; traction control system sensitivity refers to the trigger threshold of the traction control system, used to control the timing of traction control system intervention; energy recovery strength refers to the energy recovery force during braking, used to control the efficiency of energy recovery and range; motor control carrier frequency refers to the carrier frequency of the motor control signal, used to control the smoothness of motor operation and noise level; adaptive cruise speed refers to the target speed of the automatic cruise function, used to control the vehicle's driving speed in cruise mode.

[0120] Optionally, the motor controller first determines the target driving mode, for example, according to the driving mode determination method in the aforementioned embodiments, determining the target driving mode as Master, Aggressive, Sport, Standard, or Eco. Then, the controller searches for the corresponding vehicle control strategy based on the target driving mode. For example, the vehicle control strategy corresponding to Master mode is: bus current limiting 100%, bus phase current 100%, PWM output frequency 100%, hard start level 100%, TCS strength 0%, TCS sensitivity 0%, energy recovery strength 0%, motor control carrier frequency 100%, and adaptive cruise speed 100%; the vehicle control strategy corresponding to Aggressive mode is: bus current limiting 100%, bus phase current 100%, PWM output frequency 90%, hard start level 90%, TCS strength 0%, TCS sensitivity 0%, energy recovery strength 0%, motor control carrier frequency 90%, and adaptive cruise speed 90%; the vehicle control strategy corresponding to Sport mode is: bus current limiting 90%, bus phase current 90%, PWM output frequency 90%, and adaptive cruise speed 90%. The control strategies for the standard model are: frequency 80%, hard start level 80%, TCS strength 50%, TCS sensitivity 50%, energy recovery intensity 0%, motor control carrier frequency 80%, and adaptive cruise speed 80%. The corresponding vehicle control strategies for the energy-saving model are: bus current limiting 90%, bus phase current 90%, PWM output frequency 70%, hard start level 70%, TCS strength 90%, TCS sensitivity 90%, energy recovery intensity 50%, motor control carrier frequency 70%, and adaptive cruise speed 70%. The corresponding vehicle control strategies for the energy-saving model are: bus current limiting 90%, bus phase current 90%, PWM output frequency 60%, hard start level 60%, TCS strength 100%, TCS sensitivity 90%, energy recovery intensity 100%, motor control carrier frequency 60%, and adaptive cruise speed 60%. Next, the controller adjusts the control parameters of the electric two-wheeler to the corresponding specified parameter values ​​according to the vehicle control strategy corresponding to the target driving mode. For example, when the target driving mode is Sport, the controller adjusts the bus current limit to 90%, the bus phase current to 90%, the PWM output frequency to 80%, the hard start level to 80%, the TCS strength to 50%, the TCS sensitivity to 50%, the energy recovery strength to 0%, the motor control carrier frequency to 80%, and the adaptive cruise speed to 80%. Finally, the controller controls the motor operation according to the adjusted control parameters to achieve power output and handling characteristics that match the driving mode.

[0121] This embodiment achieves precise matching between control parameters and driving mode by controlling the electric two-wheeler's parameters to specified values ​​according to the vehicle control strategy corresponding to the target driving mode. The vehicle control strategy is optimized based on the characteristics of different driving modes; for example, the Master mode provides maximum power output and minimal energy recovery, while the Energy-Saving mode provides moderate power output and maximum energy recovery, ensuring the matching of control parameters with driving style. Control parameters include various parameters such as bus current limiting, bus phase current, pulse width modulation output frequency, hard start level, traction control system strength, traction control system sensitivity, energy recovery intensity, motor control carrier frequency, and adaptive cruise speed, comprehensively adjusting the vehicle's power output, handling characteristics, safety protection, and range. Adjusting control parameters according to the vehicle control strategy corresponding to the target driving mode avoids the tediousness and errors of manual adjustment, achieving automatic matching of control parameters and improving driving experience and vehicle performance. This solves the problem of insufficient precise matching of control parameter adjustment methods with driving modes in related technologies, improving driving experience and vehicle performance.

[0122] In one exemplary embodiment, existing control parameter adjustment methods lack real-time monitoring and anomaly handling of the system state, failing to promptly detect and address vehicle anomalies, leading to safety hazards and a decline in driving experience. To address this issue, this embodiment displays the adjusted control parameters of the electric two-wheeler on its interface and monitors the system state in real time. In response to system state anomalies, it collects corresponding anomaly information, reports this information to a cloud server, and executes preset protection strategies. This achieves real-time monitoring of the system state and timely handling of anomalies, improving vehicle safety and driving experience.

[0123] In some embodiments, the above method further includes: displaying the adjusted control parameters of the electric two-wheeler on the display interface of the electric two-wheeler, and monitoring the system status of the electric two-wheeler in real time; in response to the system status indicating that there is an abnormal event in the electric two-wheeler, collecting the abnormal information corresponding to the abnormal event, reporting the abnormal information to the cloud server, and executing a preset protection strategy.

[0124] The system status refers to the working status and operating parameters of each subsystem of the electric two-wheeler, including the motor status, battery status, controller status, sensor status, etc., which are used to assess whether the vehicle is operating normally.

[0125] Abnormal events refer to unusual situations that occur during the operation of electric two-wheelers, such as motor overcurrent, battery overvoltage, sensor failure, and communication anomalies, and are used to trigger abnormal handling procedures. Abnormal information refers to relevant data recorded when an abnormal event occurs, including the abnormality type, occurrence time, location, and relevant parameter values, and is used to analyze the cause of the abnormality and handle the abnormal situation.

[0126] Preset protection strategies refer to pre-defined strategies for handling abnormal events, including reducing power, limiting speed, stopping operation, and restarting the system, which are used to protect the safety of vehicles and drivers in abnormal situations.

[0127] Optionally, the motor controller first displays the adjusted control parameters on the electric two-wheeler's display interface. For example, it displays control parameters such as 90% bus current limiting, 80% PWM output frequency, 50% TCS intensity, and 0% energy recovery intensity to the driver via an instrument panel or mobile app, allowing the driver to understand the vehicle's control status in real time. Then, the controller monitors the electric two-wheeler's system status in real time, acquiring system status information by reading data from the motor status register, battery management system, and sensors, such as monitoring motor temperature, battery voltage, bus current, and wheel speed sensor signals. Next, the controller determines whether the system status indicates an abnormal event in the electric two-wheeler, such as whether the motor temperature exceeds a threshold, whether the battery voltage is below a threshold, or whether the sensor signals are abnormal. Finally, when the system status indicates an abnormal event in the electric two-wheeler, the controller collects the corresponding abnormal information, such as recording the abnormal type as motor overcurrent, the occurrence time as the current timestamp, the occurrence location as the motor module, and the relevant parameter values ​​as the current temperature and current values. Then, the controller reports the abnormal information to the cloud server and sends the abnormal information to the cloud database through the wireless communication module for subsequent data analysis and fault diagnosis. At the same time, the controller executes preset protection strategies, such as reducing the motor output power when the motor is overcurrent, limiting the vehicle speed when the battery is overvoltage, stopping related functions when the sensor fails, and stopping the vehicle from running when there is a serious abnormality.

[0128] For example, during a ride, the motor controller displays adjusted control parameters on the instrument panel, including bus current limit of 90%, PWM output frequency of 80%, TCS intensity of 50%, and energy recovery intensity of 0%. The controller monitors the system status in real time and detects that the motor temperature has risen from the normal value of 60℃ to 95℃, exceeding the preset abnormal threshold of 90℃. The controller determines that the system status indicates an abnormal event in the electric two-wheeler, namely, a motor overcurrent anomaly. The controller collects the corresponding abnormal information, including the anomaly type as motor overcurrent, the occurrence time as [date and time], the location as the motor module, and the relevant parameter values ​​as the current temperature of 95℃ and the current value of 80A. Then, the controller reports the abnormal information to the cloud server, sending it to the cloud database via the 4G communication module. Simultaneously, the controller executes a preset protection strategy, reducing the motor output power from 100% to 50%, limiting the vehicle's maximum speed to 30km / h, and displaying a "motor overcurrent" warning message on the instrument panel to remind the driver to pay attention to safety.

[0129] This embodiment displays the adjusted control parameters of the electric two-wheeler on its interface and monitors the system status in real time. In response to abnormal events indicating system status issues, it collects corresponding information, reports this information to a cloud server, and executes preset protection strategies. This achieves real-time monitoring of the system status and timely handling of abnormal situations. Displaying the control parameters on the interface allows the driver to easily understand the vehicle's control status, improving the transparency and convenience of the driving experience. Real-time system status monitoring enables timely detection of vehicle anomalies, preventing potential safety hazards. Collecting and reporting abnormal information to the cloud server in response to abnormal events allows for remote storage and analysis of abnormal data, facilitating subsequent fault diagnosis and system optimization. Executing preset protection strategies allows for timely measures to protect the vehicle and driver in abnormal situations, preventing the escalation of the anomaly. This solves the problem of lacking real-time monitoring and anomaly handling in related technologies for control parameter adjustment methods, improving vehicle safety and the driving experience.

[0130] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0131] According to another aspect of the embodiments of this application, an electric two-wheeled vehicle control device is also provided. This electric two-wheeled vehicle control device can be used to implement the electric two-wheeled vehicle control method provided in the above embodiments, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0132] Figure 4 This is a structural block diagram of an optional electric two-wheeled vehicle control device according to an embodiment of this application, such as... Figure 4 As shown, the electric two-wheeler control device includes:

[0133] The data acquisition module 402 is used to collect multiple sets of vehicle performance parameters at a preset acquisition frequency during the riding of the electric two-wheeler.

[0134] The statistics module 404 is used to determine the index value corresponding to multiple preset indicators in each set of vehicle performance parameters based on each set of vehicle performance parameters in multiple sets of vehicle performance parameters, and to classify the index value of the same preset indicator in multiple sets of vehicle performance parameters into multiple preset indicator ranges.

[0135] The control module 406 is used to determine the target driving mode of the electric two-wheeled vehicle from multiple driving modes based on the distribution of index values ​​within multiple preset index ranges corresponding to each preset index in multiple sets of vehicle performance parameters, and to adjust the control parameters of the electric two-wheeled vehicle according to the preset control strategy corresponding to the target driving mode.

[0136] It should be noted that the acquisition module 402 in this embodiment can be used to execute the above step S202, the statistics module 404 in this embodiment can be used to execute the above step S204, and the control module 406 in this embodiment can be used to execute the above step S206.

[0137] In one exemplary embodiment, the number of preset index ranges corresponding to each preset index is equal; the multiple preset indexes include multiple performance limit indexes and multiple performance change rate indexes; the multiple performance limit indexes refer to the maximum performance value of the electric two-wheeler; the multiple performance change rate indexes refer to the rate of change of the vehicle performance of the electric two-wheeler; the statistics module 404 is further configured to classify the index values ​​of the same target performance limit index in each group of vehicle performance parameters into the multiple preset index ranges corresponding to the target performance limit index, and classify the index values ​​of the same target performance change rate index in each group of vehicle performance parameters into the multiple preset index ranges corresponding to the target performance change rate index.

[0138] In an exemplary embodiment, each preset index range among the multiple preset index ranges corresponding to each preset index does not overlap; each preset index range corresponds one-to-one with multiple driving modes; each preset index range corresponds to a preset level; the control module 406 is further configured to determine the target driving mode of the electric two-wheeled vehicle as the driving mode corresponding to the specified preset index range in response to the number of index values ​​within the specified preset index range corresponding to each preset index in multiple sets of vehicle performance parameters being greater than a preset number threshold; the specified preset index range corresponding to each preset index refers to the index range with a specified preset level among the multiple preset index ranges corresponding to each preset index.

[0139] In an exemplary embodiment, the control module 406 is further configured to determine the target driving mode of the electric two-wheeled vehicle as the first driving mode in response to the specified preset index range corresponding to each preset index being the first preset index range; the first preset index range corresponding to each preset index refers to the index range with the largest boundary value among the multiple preset index ranges corresponding to each preset index; the first driving mode refers to the mode with the maximum power output and the lowest energy recovery efficiency.

[0140] In an exemplary embodiment, the control module 406 is further configured to determine the target driving mode of the electric two-wheeled vehicle as the second driving mode in response to the fact that the number of index values ​​within the second preset index range corresponding to each preset index in the plurality of vehicle performance parameters is greater than a second quantity threshold; the second preset index range corresponding to each preset index refers to the index range with the largest boundary value among the plurality of preset index ranges corresponding to each preset index, excluding the first preset index range; the second driving mode refers to the mode that balances power output and energy recovery efficiency.

[0141] In an exemplary embodiment, the control module 406 is further configured to determine the target driving mode of the electric two-wheeled vehicle as the third driving mode in response to the fact that the number of index values ​​within the third preset index range corresponding to each preset index in the plurality of vehicle performance parameters is greater than a third quantity threshold; the third preset index range corresponding to each preset index refers to the index range with the largest boundary value among the plurality of preset index ranges corresponding to each preset index, excluding the first preset index range and the second preset index range; the third driving mode refers to the mode with the highest energy recovery efficiency.

[0142] In an exemplary embodiment, the control module 406 is further configured to determine the target driving mode of the electric two-wheeled vehicle as the fourth driving mode in response to the fact that the number of index values ​​within the fourth preset index range corresponding to each preset index in the plurality of vehicle performance parameters is greater than the fourth quantity threshold; the fourth preset index range corresponding to each preset index refers to the index range with the largest boundary value among the plurality of preset index ranges corresponding to each preset index, excluding the first preset index range, the second preset index range, and the third preset index range; the fourth driving mode refers to the mode in which the power output is greater than that of the third driving mode and the energy recovery efficiency is greater than that of the third driving mode.

[0143] In an exemplary embodiment, the control module 406 is further configured to control the control parameters of the electric two-wheeler to reach specified parameter values ​​according to the vehicle control strategy corresponding to the target driving mode; the control parameters include at least one of the following parameters: bus current limiting, bus phase current, pulse width modulation output frequency, hard start level, traction control system strength, traction control system sensitivity, energy recovery strength, motor control carrier frequency, and adaptive cruise speed.

[0144] In an exemplary embodiment, the control module 406 is further configured to display the adjusted control parameters of the electric two-wheeler on the display interface of the electric two-wheeler, and monitor the system status of the electric two-wheeler in real time; in response to the system status indicating that there is an abnormal event in the electric two-wheeler, collect the abnormal information corresponding to the abnormal event, report the abnormal information to the cloud server, and execute the preset protection strategy.

[0145] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0146] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0147] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A control method for an electric two-wheeled vehicle, characterized in that, include: While the electric two-wheeler is in operation, multiple sets of vehicle performance parameters are collected according to a preset collection frequency. Based on each set of vehicle performance parameters, determine the index value corresponding to each preset index among multiple preset indicators in each set of vehicle performance parameters, and classify the index values ​​of the same preset index in the multiple sets of vehicle performance parameters into multiple preset index ranges. Based on the distribution of index values ​​within multiple preset index ranges corresponding to each preset index in the multiple sets of vehicle performance parameters, the target driving mode adopted by the driver of the electric two-wheeler is determined from multiple driving modes, and the control parameters of the electric two-wheeler are adjusted according to the vehicle control strategy corresponding to the target driving mode; one driving mode in the multiple driving modes corresponds to one vehicle control strategy; one driving mode in the multiple driving modes is used to characterize a driving style of the driver.

2. The method according to claim 1, characterized in that, The number of preset index ranges corresponding to each preset index is equal; the multiple preset indexes include multiple performance limit indexes and multiple performance change rate indexes; the multiple performance limit indexes refer to the maximum performance value of the electric two-wheeler. The aforementioned multiple performance change rate indicators refer to the rate of change of the vehicle performance of the electric two-wheeler; The step of classifying the index values ​​of the same preset index among the multiple sets of vehicle performance parameters into multiple preset index ranges includes: The index values ​​of the same target performance limit index in each group of vehicle performance parameters are classified into multiple preset index ranges corresponding to the target performance limit index, and the index values ​​of the same target performance change rate index in each group of vehicle performance parameters are classified into multiple preset index ranges corresponding to the target performance change rate index.

3. The method according to claim 1, characterized in that, Each preset index range does not overlap with the multiple preset index ranges corresponding to each preset index; each preset index range corresponds one-to-one with the multiple driving modes; each preset index range corresponds to a preset level; determining the target driving mode adopted by the electric two-wheeler from the multiple driving modes based on the distribution of index values ​​within the multiple preset index ranges corresponding to each preset index in the multiple sets of vehicle performance parameters includes: In response to the fact that the number of index values ​​within a specified preset index range corresponding to each preset index in the multiple sets of vehicle performance parameters is greater than a preset number threshold, the target driving mode of the electric two-wheeler is determined to be the driving mode corresponding to the specified preset index range; the specified preset index range corresponding to each preset index refers to the index range of a specified preset level among the multiple preset index ranges corresponding to each preset index.

4. The method according to claim 3, characterized in that, The method further includes: In response to the specified preset index range corresponding to each preset index being the first preset index range, the target driving mode of the electric two-wheeled vehicle is determined as the first driving mode; the first preset index range corresponding to each preset index refers to the index range with the largest boundary value among the multiple preset index ranges corresponding to each preset index; the first driving mode refers to the mode with the maximum power output and the lowest energy recovery efficiency.

5. The method according to claim 4, characterized in that, The method further includes: In response to the fact that the number of index values ​​within the second preset index range corresponding to each preset index in the multiple sets of vehicle performance parameters is greater than the second quantity threshold, the target driving mode of the electric two-wheeled vehicle is determined to be the second driving mode; the second preset index range corresponding to each preset index refers to the index range with the largest boundary value among the multiple preset index ranges corresponding to each preset index, excluding the first preset index range; the second driving mode refers to the mode that balances power output and energy recovery efficiency.

6. The method according to claim 5, characterized in that, The method further includes: In response to the fact that the number of index values ​​within the third preset index range corresponding to each preset index in the multiple sets of vehicle performance parameters is greater than the third quantity threshold, the target driving mode of the electric two-wheeled vehicle is determined to be the third driving mode; the third preset index range corresponding to each preset index refers to the index range with the largest boundary value among the multiple preset index ranges corresponding to each preset index, excluding the first preset index range and the second preset index range; the third driving mode refers to the mode with the highest energy recovery efficiency.

7. The method according to claim 6, characterized in that, The method further includes: In response to the fact that the number of index values ​​within the fourth preset index range corresponding to each preset index in the multiple sets of vehicle performance parameters is greater than the fourth quantity threshold, the target driving mode of the electric two-wheeled vehicle is determined to be the fourth driving mode; the fourth preset index range corresponding to each preset index refers to the index range with the largest boundary value among the multiple preset index ranges corresponding to each preset index, excluding the first preset index range, the second preset index range, and the third preset index range; the fourth driving mode refers to the mode in which the power output is greater than that of the third driving mode and the energy recovery efficiency is greater than that of the third driving mode.

8. The method according to any one of claims 1 to 7, characterized in that, Adjusting the control parameters of the electric two-wheeler according to the vehicle control strategy corresponding to the target driving mode includes: According to the vehicle control strategy corresponding to the target driving mode, the control parameters of the electric two-wheeler are controlled to reach the specified parameter values; the control parameters include at least one of the following parameters: bus current limiting, bus phase current, pulse width modulation output frequency, hard start level, traction control system strength, traction control system sensitivity, energy recovery strength, motor control carrier frequency, and adaptive cruise speed.

9. The method according to any one of claims 1 to 7, characterized in that, The method further includes: The adjusted control parameters of the electric two-wheeler are displayed on the display interface of the electric two-wheeler, and the system status of the electric two-wheeler is monitored in real time. In response to the system status indicating an abnormal event in the electric two-wheeler, the system collects the abnormal information corresponding to the abnormal event, reports the abnormal information to the cloud server, and executes a preset protection strategy.

10. A control device for an electric two-wheeled vehicle, characterized in that, include: The data acquisition module is used to collect multiple sets of vehicle performance parameters at a preset acquisition frequency during the riding of the electric two-wheeler. The statistics module is used to determine the index value corresponding to multiple preset indicators in each set of vehicle performance parameters based on each set of vehicle performance parameters, and to classify the index value of the same preset indicator in the multiple sets of vehicle performance parameters into multiple preset indicator ranges. The control module is used to determine the target driving mode of the electric two-wheeler from multiple driving modes based on the distribution of index values ​​within multiple preset index ranges corresponding to each preset index in the multiple sets of vehicle performance parameters, and to adjust the control parameters of the electric two-wheeler according to the preset control strategy corresponding to the target driving mode.

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