A long endurance control method and device applied to an electric two-wheeled vehicle

Abstract

The application relates to the technical field of electric vehicles, in particular to a long-endurance control method and equipment applied to an electric two-wheeled vehicle, which can acquire real-time vehicle condition data of the electric two-wheeled vehicle, including vehicle speed data, vehicle positioning data, user operation data, motor operation data and slope; according to the real-time vehicle condition data, key features are extracted; according to the key features, the current application scene of the electric two-wheeled vehicle is identified according to preset priorities of application scenes; and according to the identified application scene, an energy recovery strategy with different intensity ranges is enabled, and the intensity range corresponds to the application scene. According to the technical scheme, the energy recovery strategy with different intensity ranges can be enabled according to the current application scene of the vehicle, the energy recovery conditions in multiple scenes are comprehensively considered, the energy recovery efficiency is improved while the user experience is ensured, and the controller strategy is accurate to multiple scenes, so that the energy is finely distributed.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, and specifically to a long-range control method and device for electric two-wheeled vehicles. Background Technology

[0002] Currently, the range of electric two-wheelers is limited by battery energy density and overall vehicle energy efficiency. Although some high-end models have implemented energy recovery functions, their actual effectiveness is limited, mainly for the following reasons:

[0003] Poor energy recovery efficiency: Most existing two-wheeled vehicles use simple threshold control methods for energy recovery (e.g., recovery starts when the vehicle speed is greater than 15 km / h and the accelerator is released), without taking into account various situations, resulting in less recovered energy, poor user experience, and even potential damage to the battery due to surge current.

[0004] The controller strategy is not precise enough: Most controllers only provide fixed modes such as "economy / standard / sport" and lack the ability to dynamically recognize and respond to various riding scenarios, making it difficult to achieve fine-grained energy allocation.

[0005] Therefore, the energy recovery function currently used in electric two-wheelers suffers from poor energy recovery efficiency and inaccurate control strategies. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a long-range control method and device for electric two-wheelers, so as to solve the problems of poor energy recovery efficiency and inaccurate control strategy in the energy recovery function applied to electric two-wheelers in the prior art.

[0007] According to a first aspect of the present invention, a long-range control method for electric two-wheeled vehicles is provided, comprising:

[0008] Acquire real-time vehicle status data for electric two-wheelers, including: vehicle speed data, vehicle location data, user operation data, motor operation data, and slope.

[0009] Based on the real-time vehicle condition data, key features are extracted; based on the key features, the current application scenario of the electric two-wheeler is identified according to the preset priority of the application scenario.

[0010] Based on the identified application scenario, energy recovery strategies with different intensity ranges are activated, where the intensity range corresponds to the application scenario.

[0011] Preferably, key features are extracted based on the real-time vehicle status data, including:

[0012] Based on vehicle speed data, the number of starts and stops is calculated using the first sliding time window;

[0013] Based on user operation data, the fluctuation of the speed control handle opening is calculated using the second sliding time window.

[0014] Based on the motor operating data, the motor load fluctuation is calculated using the second sliding time window.

[0015] Based on user operation data, the braking frequency is calculated using the third sliding time window;

[0016] The vehicle speed data, number of starts and stops, opening degree fluctuation, motor load fluctuation, braking frequency, motor current, and speed regulation are integrated into the opening degree as key features.

[0017] Preferably, the current application scenario of the electric two-wheeler is identified according to the preset priority of the application scenario, including:

[0018] If the slope is ≥5°, the motor current is ≥0.7×rated current, the vehicle speed is ≤25km / h, and the speed control handle opening is ≥60%, then the current application scenario for electric two-wheelers is continuous uphill road conditions.

[0019] If the continuous uphill road condition is not met, then the following judgment is made:

[0020] If the slope is ≤-3°, the motor current is ≤0.1×rated current, the vehicle speed is ≥15km / h, and the braking frequency is ≤2 times, then the current application scenario for electric two-wheelers is long downhill road conditions.

[0021] If the long downhill road condition is not met, then the following judgment is made:

[0022] If the vehicle speed is ≤15km / h, the number of start-stop times is ≥1, the speed control handle opening fluctuation is >50%, and the braking frequency is ≥1, then the current application scenario for electric two-wheelers is urban congested road conditions.

[0023] If the urban traffic congestion conditions are not met, the following judgment will be made:

[0024] If the vehicle speed is ∈ [20,45] km / h, and the motor load fluctuation is ≤10%, the slope is ≤3°, the opening fluctuation is ≤15%, and the positioning prediction conditions are met, then the current application scenario of the electric two-wheeler is flat road cruising.

[0025] The positioning prediction conditions are as follows: based on the current position in the vehicle positioning data, calculate the slope standard deviation of the path 1km ahead from the map database; based on the current vehicle speed and direction, calculate the path the vehicle will take within 30 seconds; if the slope standard deviation is ≤1°, and it is determined from the map database that the path does not pass through an intersection, then the positioning prediction conditions are met.

[0026] Preferably, the method further includes:

[0027] After each preset time period, the application scenario of the electric two-wheeler is determined. If the application scenario is consistent in two consecutive determinations, the application scenario of the electric two-wheeler is switched to the determined application scenario.

[0028] When the application scenario of the electric two-wheeler changes, the intensity of the energy recovery strategy is linearly changed within a preset time.

[0029] Preferably, the method further includes:

[0030] Obtain battery SOC and battery temperature;

[0031] If the battery SOC > 95%, reduce the intensity of the current energy recovery strategy by 50%.

[0032] If the battery temperature is >60℃, or if real-time vehicle condition data of the electric two-wheeler cannot be obtained, the energy recovery strategy will be disabled.

[0033] Preferably, the method further includes:

[0034] Obtain the mode switching command sent by the user, and switch the operating mode of the electric two-wheeler to super running mode, balanced mode or extreme speed mode according to the mode switching command;

[0035] In Super Run mode, the speed of the electric two-wheeler is limited to below the preset maximum speed, and the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to the highest value within the intensity range.

[0036] In balanced mode, the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to the preset median range within the intensity range;

[0037] In high-speed mode, the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to a preset low range within the intensity range.

[0038] Preferably, the method further includes:

[0039] When the electric two-wheeler is cruising on a flat road, if the vehicle speed is between 30-35 km / h for more than 60 seconds, or if the speed control lever is open at 40%-60% for more than 60 seconds, then the electric two-wheeler's operating mode will be switched to Super Run mode.

[0040] When an electric two-wheeler is on any road condition, if the speed control lever opening is greater than 90% for more than 1 second, or if the electric two-wheeler's acceleration is greater than 1.5 m / s², the following conditions will be met: 2 Then the electric two-wheeler's operating mode will be switched to high-speed mode;

[0041] When the electric two-wheeler is on a continuous uphill road, if the slope is greater than 8% for more than 10 seconds, or the motor current is greater than 15A for more than 30 seconds, the operating mode of the electric two-wheeler will be switched to Super Run mode.

[0042] When an electric two-wheeler is in congested urban traffic, if the duration of the speed being less than 15 km / h is greater than 10 seconds, or the frequency of starting and stopping is greater than 3 times per minute, the operating mode of the electric two-wheeler will be switched to balanced mode.

[0043] Preferably, the method further includes:

[0044] The real-time vehicle condition data and the vehicle condition data within a preset time period are input into a threshold-based lightweight decision tree model for trigger scenario prediction.

[0045] Adjust the intensity of the energy recovery strategy based on the predicted triggering scenarios;

[0046] If the triggering scenario does not occur within the preset time, the energy recovery strategy will be restored to its original strength.

[0047] Preferably, the method further includes: predicting changes in motor operating conditions over the next 5 control cycles based on the acquired gyroscope attitude data and motor operating data;

[0048] Maximizing motor efficiency is used as the objective function, and current, voltage, and temperature constraints are set.

[0049] Using the objective function and changes in motor operating conditions, the optimal driving parameters are solved through a quadratic programming algorithm;

[0050] The optimal driving parameters are converted into motor driving signals using space vector pulse width modulation technology, which control the inverter output voltage vector to drive the flat wire permanent magnet synchronous hub motor.

[0051] According to a second aspect of the present invention, a long-range control device for an electric two-wheeled vehicle is provided, comprising:

[0052] The main controller and the memory connected to the main controller;

[0053] The memory stores program instructions;

[0054] The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0055] The technical solution provided by this invention may include the following beneficial effects:

[0056] It is understood that the technical solution presented in this invention can acquire real-time vehicle condition data of an electric two-wheeler, including: vehicle speed data, vehicle positioning data, user operation data, motor operation data, and slope; extract key features based on the real-time vehicle condition data; identify the current application scenario of the electric two-wheeler according to the key features and a preset priority of the application scenario; and activate energy recovery strategies of different intensity ranges based on the identified application scenario, wherein the intensity range corresponds to the application scenario. This technical solution can activate energy recovery strategies of different intensity ranges according to the current application scenario of the vehicle, comprehensively considering the energy recovery situation under multiple scenarios, improving energy recovery efficiency while ensuring user experience, and the controller strategy is accurate to multiple scenarios, realizing fine-grained energy allocation.

[0057] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0059] Figure 1 This is a schematic diagram illustrating the steps of a long-range control method applied to an electric two-wheeler according to an exemplary embodiment;

[0060] Figure 2 This is a schematic diagram of a long-range control method according to an exemplary embodiment;

[0061] Figure 3 This is a schematic diagram illustrating a mode switching process according to an exemplary embodiment. Detailed Implementation

[0062] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0063] In one embodiment, Figure 1 This is a schematic diagram illustrating the steps of a long-range control method applied to an electric two-wheeler according to an exemplary embodiment. See also... Figure 1 According to a first aspect of the present invention, a long-range control method for electric two-wheeled vehicles is provided, comprising:

[0064] Step S11: Obtain real-time vehicle status data of the electric two-wheeler, including: vehicle speed data, vehicle positioning data, user operation data, motor operation data, and slope.

[0065] Step S12: Extract key features based on the real-time vehicle condition data; based on the key features and according to the preset priority of the application scenario, identify the current application scenario of the electric two-wheeler.

[0066] Step S13: Based on the identified application scenario, enable energy recovery strategies with different intensity ranges, where the intensity range corresponds to the application scenario.

[0067] Energy recovery strategies applied to electric two-wheelers, such as converting the kinetic energy of the vehicle into electrical energy through the motor, and then processing it through the controller to recharge the battery, can have their intensity quantified by recovering torque.

[0068] It is understood that the technical solution shown in this embodiment can activate energy recovery strategies with different intensity ranges according to the current application scenario of the vehicle, comprehensively considering the energy recovery situation under multiple scenarios. While ensuring user experience, it improves energy recovery efficiency, and the controller strategy is precise for multiple scenarios, achieving refined energy allocation. This can significantly improve the driving range of electric two-wheelers (including electric bicycles, electric mopeds, and electric motorcycles). This solution emphasizes engineering feasibility and is applicable to current mainstream electric two-wheeler platforms.

[0069] In one specific embodiment, this technical solution can intelligently and dynamically identify multi-dimensional application scenarios.

[0070] In practical applications, the hardware structure consists of a Global Positioning System (GPS), a gyroscope, a microcontroller unit (MCU), a motor Hall sensor, and a temperature sensor. See the flowchart below. Figure 2 .

[0071] The controller uses threshold determination and feature matching algorithms.

[0072] Firstly, it can collect real-time vehicle status data using components such as gyroscopes and sensors. The GPS outputs real-time location, vehicle speed (km / h), and altitude (for slope calculation); the gyroscope outputs the vehicle attitude angle (°) to assist in verifying the slope; the motor Hall sensor outputs the vehicle speed (km / h); the throttle sensor outputs the opening percentage (0-100%); the brake sensor outputs a switch signal (0 / 1, used to detect emergency braking); and the temperature sensor outputs the ambient / battery temperature (°C), used only for safety monitoring (not directly involved in status determination).

[0073] The data is then preprocessed. First, timestamp alignment is performed, assigning precise timestamps to each sensor data point to eliminate asynchronous errors. Next, noise filtering is applied, using a first-order low-pass filter (time constant 200ms) to eliminate high-frequency jitter (e.g., vehicle speed fluctuations within ±2km / h are considered noise). Finally, the preprocessed data stream is output, and key features are extracted using this data.

[0074] In practice, key features are extracted based on the real-time vehicle condition data, including:

[0075] The number of starts and stops is calculated using the first sliding time window based on vehicle speed data.

[0076] For example, if the first sliding time window is a 5-second window, the start-stop count is defined as the count of events when the vehicle speed accelerates from 0 to greater than 5 km / h, or when the vehicle speed decelerates from greater than 5 km / h to 0.

[0077] Based on user operation data, the fluctuation of the speed control handle opening is calculated using the second sliding time window. Based on motor operation data, the fluctuation of the motor load is calculated using the second sliding time window.

[0078] For example, the second sliding time window is a 10-second window. The speed control opening fluctuation can be calculated as follows: (maximum opening value - minimum opening value) / average opening value × 100%.

[0079] The calculation method for motor load fluctuation is: (maximum current - minimum current) / average current × 100% (load = current / rated current).

[0080] The braking frequency is calculated using a third sliding time window based on user operation data.

[0081] For example, the third sliding time window is a 30-second window, and the braking frequency refers to the number of times the brakes are applied within 30 seconds.

[0082] For other features, all are windowless real-time features. The slope (°) is a fusion of GPS altitude change rate (Δaltitude / Δdistance) and gyroscope attitude angle, updated every 100ms. The vehicle speed (km / h), motor current (A), and speed control lever opening (%) are instantaneous values.

[0083] For window sliding: every 100ms, the oldest data is removed, new data is added, and features are recalculated (e.g., a 10-second window uses the most recent 100 samples).

[0084] Feature denoising should also be performed: apply a moving average (5 samples in a window) to the fluctuation features to avoid instantaneous anomalies (such as sudden changes in opening caused by a single rapid acceleration).

[0085] Ultimately, vehicle speed data, number of starts and stops, opening degree fluctuations, motor load fluctuations, braking frequency, motor current, and speed regulation are integrated into key features. In specific applications, these key features are updated every 100ms.

[0086] In practice, the current application scenarios of electric two-wheelers are identified according to the preset priority of the application scenarios, including:

[0087] If the slope is ≥5°, the motor current is ≥0.7×rated current, the vehicle speed is ≤25km / h, and the speed control handle opening is ≥60%, then the current application scenario for electric two-wheelers is continuous uphill road conditions.

[0088] If the continuous uphill road condition is not met, then the following judgment is made:

[0089] If the slope is ≤-3°, the motor current is ≤0.1×rated current, the vehicle speed is ≥15km / h, and the braking frequency is ≤2 times, then the current application scenario for electric two-wheelers is long downhill road conditions.

[0090] If the long downhill road condition is not met, then the following judgment is made:

[0091] If the vehicle speed is ≤15km / h, the number of start-stop times is ≥1, the speed control handle opening fluctuation is >50%, and the braking frequency is ≥1, then the current application scenario for electric two-wheelers is urban congested road conditions.

[0092] If the urban traffic congestion conditions are not met, the following judgment will be made:

[0093] If the vehicle speed is ∈ [20,45] km / h, and the motor load fluctuation is ≤10%, the slope is ≤3°, the opening fluctuation is ≤15%, and the positioning prediction conditions are met, then the current application scenario of the electric two-wheeler is flat road cruising.

[0094] The positioning prediction conditions are as follows: based on the current position in the vehicle positioning data, calculate the slope standard deviation of the path 1km ahead from the map database; based on the current vehicle speed and direction, calculate the path the vehicle will take within 30 seconds; if the slope standard deviation is ≤1° (considered as "straight road with no slope change"), and it is determined from the map database that the path does not pass through an intersection, then the positioning prediction conditions are met.

[0095] The above conditions are prioritized. For example, if both urban traffic congestion and continuous uphill conditions are met, the continuous uphill conditions will be prioritized.

[0096] Preferably, a conditional tolerance can also be set: each threshold is set with a tolerance of ±5% (e.g., a vehicle speed of 14.5-15.5 km / h is considered ≤15 km / h) to avoid critical vibrations. A timeout reset can be set: if the state duration is <500ms (e.g., a momentary downhill slope), the judgment will not be triggered to prevent misjudgment.

[0097] Preferably, the method can also be configured with smooth state transition and anti-shake processing:

[0098] Every first preset time period (e.g., 100ms), the application scenario of the electric two-wheeler is determined. If the application scenario is consistent in two consecutive determinations, the application scenario of the electric two-wheeler is switched to the determined application scenario. This technical solution performs confidence verification, and the new state only takes effect after two consecutive consistent determinations (200ms window) to avoid sensor noise causing jumps.

[0099] When the application scenario of the electric two-wheeler changes, the intensity of the energy recovery strategy is linearly changed within a preset time. This technical solution provides a gradual output, and when the state changes, the energy recovery intensity transitions linearly within 1-2 seconds (for example, from an energy intensity of 3 N·m to an energy intensity of 5 N·m, increasing by 0.2 N·m every 100 ms), avoiding abrupt changes.

[0100] Preferably, it can also monitor user feedback signals in real time (such as collecting "jerkiness" scores via the APP). If discomfort is detected, the retracement intensity is temporarily reduced by 10% and the timer is reset. Finally, a stable riding status indicator is obtained and input into the controller.

[0101] In practice, the method also sets up security boundaries and exception handling. This scheme is independent of state determination and the security boundaries are monitored in real time by the MCU.

[0102] First, obtain the battery SOC and battery temperature.

[0103] If the battery SOC is greater than 95%, the intensity of the current energy recovery strategy will be reduced by 50% to avoid overcharging, especially when going downhill.

[0104] If the battery temperature is >60℃, or if real-time vehicle condition data of the electric two-wheeler cannot be obtained, the energy recovery strategy will be disabled, or the system will revert to the "default state" (fixed 2 N·m recovery).

[0105] Once the sensor signal recovers and stabilizes for 5 seconds, dynamic recognition is reactivated.

[0106] In one embodiment, energy recovery is performed and feedback is initiated based on the final application scenario of the electric two-wheeler, specifically as follows:

[0107] In congested urban traffic, the intensity range of the energy recovery strategy is 3-5 N·m, which is considered low to medium intensity.

[0108] On flat roads, the energy recovery strategy has an intensity range of 5-8 N·m, which is considered medium intensity. If the road surface is detected to be slippery by the difference in speed between the front and rear wheels, the energy recovery will be automatically reduced by 20%.

[0109] On long downhill roads, the intensity range of the energy recovery strategy is 15-25 N·m, which is considered high intensity. When the battery SOC > 80%, it decreases linearly.

[0110] The energy recovery strategy is in an intensity range of 0 N·m under continuous uphill road conditions, and energy recovery is not enabled.

[0111] The motor controller executes torque commands and feeds back the actual torque to the MCU.

[0112] In practical applications, closed-loop correction can also be performed, comparing the target torque with the actual torque. If the error is >10%, parameters are fine-tuned (e.g., increasing the weight of the Hall sensor). Efficiency is recorded, and the recovered energy (kWh) is calculated every 5 seconds for subsequent optimization. The final output shows the actual energy recovery intensity, which affects vehicle dynamics.

[0113] Preferably, this technical solution can also perform self-learning and parameter optimization, which is a background task that can be executed once every 5 minutes.

[0114] During idle periods, the MCU analyzes historical data, clusters user cycling habits (such as the gradient distribution of commuting routes), and dynamically adjusts thresholds (e.g., when frequent misjudgments of congestion occur, the threshold for the number of starts and stops is increased to 2 times / 5 seconds). It also integrates GPS map updates: downloading the latest gradient data to optimize prediction accuracy. No restart is required; the new thresholds take effect in the next judgment cycle. Finally, the optimized algorithm parameters are output, improving long-term recognition accuracy.

[0115] In another embodiment, a multi-mode cooperative controller strategy can also be executed based on the above embodiments, see [link to relevant documentation]. Figure 3 The controller achieves mode coordination through "manual switching triggering + automatic scene switching" and optimizes the control strategy by combining "rule judgment + short-term prediction".

[0116] When the vehicle is in motion, it is divided into three modes: Super Run, Balanced, and Extreme Speed. The preferred Super Run mode limits the maximum speed (e.g., less than or equal to 35 km / h) and optimizes the motor's operating point to its highest efficiency range, maximizing energy recovery. Balanced mode (default) balances performance and range, dynamically adjusting based on the scenario. Extreme Speed ​​mode prioritizes acceleration needs, activating energy recovery only when necessary.

[0117] The division into three modes complements the vehicle application scenarios and together constitutes the long-range control method.

[0118] For manual trigger switching, the specific steps are as follows:

[0119] The system receives a mode switching command sent by the user and switches the operating mode of the electric two-wheeler to either Super Run mode, Balanced mode, or Extreme Speed ​​mode based on the command.

[0120] For example, users can switch modes using the "mode button" on the switch (a short press switches modes, while a long press for 3 seconds resets the mode to the default mode). At this time, the instrument will display the corresponding icon (for example, Super Run = 1, Balanced = 2, Extreme Speed ​​= 3).

[0121] In the Super Run mode, the speed of the electric two-wheeler is limited to below the preset maximum speed (e.g., less than or equal to 35km / h). At this time, the motor operating point is optimized to the highest efficiency zone, and the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to the highest value within the intensity range to maximize the recovery intensity.

[0122] In balanced mode, the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to the preset median range within the intensity range to balance performance and battery life, and is dynamically adjusted according to the scenario.

[0123] In high-speed mode, the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to the preset low range within the intensity range. In addition, in this mode, priority is given to responding to acceleration needs, and energy recovery is only activated when necessary.

[0124] For example, assuming the energy recovery strategy intensity range is 15-25 N·m on a long downhill road, if it is in the super-powered running mode, the intensity range should be 22-25 N·m, and if it is in the balanced mode, it should be 18-21 N·m.

[0125] A switching limit should also be set. When the vehicle is at high speed (vehicle speed > 50km / h), if you want to switch from the extreme speed mode to the super speed mode, you must first reduce the vehicle speed to ≤ 40km / h through energy recovery before performing mode parameter adjustment to avoid a sudden drop in vehicle speed.

[0126] Preferably, it can also have a memory function, so that the vehicle will default to the balanced mode when restarted after a power outage. If the last manually switched mode was the Super Run / Extreme Speed ​​mode and the driving mileage was less than 5km, the last used mode can be remembered.

[0127] For the automatic switching process, the method further includes:

[0128] Based on the real-time vehicle condition data, it is determined whether the switching criteria are met. If they are met, the operating mode of the electric two-wheeler is switched to the corresponding mode.

[0129] Specific examples are as follows:

[0130] When the electric two-wheeler is cruising on a flat road, if the vehicle speed is between 30-35 km / h for more than 60 seconds, or if the speed control lever is open at 40%-60% for more than 60 seconds, then the electric two-wheeler's operating mode will be switched to Super Run mode.

[0131] When an electric two-wheeler is on any road condition, if the speed control lever opening is greater than 90% for more than 1 second, or if the electric two-wheeler's acceleration is greater than 1.5 m / s², the following conditions will be met: 2 If the electric two-wheeler is switched to high-speed mode, the energy recovery intensity will be reduced to a low range or turned off to ensure that the vehicle has sufficient power output.

[0132] When the electric two-wheeler is on a continuous uphill road, if the slope is greater than 8% for more than 10 seconds, or the motor current is greater than 15A for more than 30 seconds, the operating mode of the electric two-wheeler will be switched to Super Run mode. Since the intensity range of the energy recovery strategy is 0 N·m when on a continuous uphill road, the energy recovery is canceled after Super Run mode is activated, and the maximum speed of the electric two-wheeler is limited so that the motor operating point is optimized to the highest efficiency range.

[0133] When an electric two-wheeler is in congested urban traffic, if the speed is less than 15 km / h for more than 10 seconds, or the number of starts and stops is more than 3 times per minute, the operating mode of the electric two-wheeler will be switched to balanced mode to take into account the jerky feeling of the vehicle.

[0134] In practice, AI models can also be introduced for predictive control:

[0135] The real-time vehicle condition data and the vehicle condition data within a preset time period are input into a threshold-based lightweight decision tree model to predict the triggering scenario; the intensity of the energy recovery strategy is adjusted according to the predicted triggering scenario; if the triggering scenario does not occur within the preset time period, the energy recovery strategy is restored to the intensity before adjustment.

[0136] The AI ​​model employs a threshold-based lightweight decision tree model, combined with moving average trend calculations, to avoid complex mathematical operations. Model inputs include real-time sensor data (such as slope, current, vehicle speed, and voltage) and short-term historical data (the central control system stores data from the last 30 seconds, with a sampling frequency of 1Hz). Outputs include predicted events and adjustment commands.

[0137] Examples of predicted scenarios: 1. When a vehicle is about to climb a hill, if the gradient is detected to increase from 4% to 5% within 10 seconds, and the current increases from 10 amps to 20 amps, the energy recovery function will be turned off in advance. 2. When a vehicle is about to enter a traffic light area, if the vehicle speed is detected to decrease from more than 40 km / h to 20 km / h within 5 seconds, and there is no change in gradient ahead, the energy recovery intensity will be increased in advance from 8 Nm to 12 Nm to recover more energy. 3. Predicted scenario: When a vehicle is about to descend a long hill, if an increase in negative gradient is detected and there is no drive current, it is predicted that a long descent will occur in 15 seconds, and the upper limit of the energy recovery intensity will be increased from 15 Nm to 25 Nm (high intensity) in advance to maximize energy recovery.

[0138] The prediction correction mechanism is set up so that if the actual scenario after prediction does not occur (e.g., the prediction is for climbing but the actual slope is decreasing), the original parameters will be restored within 3 seconds to avoid erroneous adjustments.

[0139] Prediction accuracy was calibrated through bench testing (e.g., hill climb prediction accuracy no less than 85%). Using a lightweight AI model, the system achieved predictive control based on real-time trends, running efficiently on a low-cost MCU. This method relies on simple rules and thresholds; future testing can further optimize the thresholds to improve accuracy.

[0140] In another embodiment, a high-efficiency flat-wire motor can also be used to improve vehicle range.

[0141] Targeting the core characteristics of flat wire permanent magnet synchronous hub motors, such as high slot fill factor, low winding resistance, excellent heat dissipation, and high efficiency under specific working conditions, the electric control drive algorithm achieves precise adaptation through three layers of logic, forming a closed loop of "motor characteristics - algorithm strategy - performance release".

[0142] Current optimization algorithm based on high slot fill factor and low winding resistance:

[0143] Dynamic current distribution strategy: In the 20%-80% load range (high-frequency operating conditions of two-wheeled vehicles), space vector pulse width modulation (SVPWM) technology is used to concentrate the current distribution to the effective conductor area of ​​the flat wire winding (avoiding the ineffective area at the ends), thereby improving the uniformity of current density distribution by 25% and reducing copper loss (P cu =I 2 R) decreases by 35%-40%.

[0144] For example, when a 350W motor is under 50% load (7.5 N•m torque), the copper loss of a traditional round wire motor is about 12W, while the copper loss of a flat wire motor is reduced to 7-8W after algorithm optimization.

[0145] High load current redundancy control: Utilizing the high current carrying capacity of flat wires, when the load is >80% (such as during incline or heavy load), the algorithm allows the current to briefly increase to 1.3 times the rated value (from 30A to 39A). At the same time, the duration is limited to ≤30s by real-time resistance monitoring (combined with temperature compensation) to avoid overcurrent losses and ensure that the efficiency remains above 88% under high load (the efficiency of traditional motors in this range is often below 80%).

[0146] Preferably, the method described can also construct an MPC algorithm control architecture, adopting a three-level architecture of "prediction-optimization-control" with a period of 10ms (synchronized with the gyroscope data acquisition frequency):

[0147] First, there's the prediction layer, which can predict changes in motor operating conditions over the next 5 control cycles (50ms) based on acquired gyroscope attitude data (tilt angle, tilt rate) and motor operating data (speed, current, temperature). For example:

[0148] If the gyroscope detection slope increases, the predicted torque demand will increase by 20%-30%. If the motor temperature T... m At temperatures above 65℃, the predicted resistance R will increase by 5%-8% due to temperature effects (based on the temperature coefficient of copper resistance = 0.0043 / ℃).

[0149] Next is the optimization layer, which can maximize motor efficiency as the objective function and set current constraints, voltage constraints, and temperature constraints: current ≤ peak 45A, voltage ≤ 1.1 times the battery rated voltage, and temperature ≤ 75℃.

[0150] Using the objective function and the changes in motor operating conditions, the optimal driving parameters (voltage, frequency) are solved by a quadratic programming algorithm.

[0151] Finally, the control layer can convert the optimal drive parameters into motor drive signals through space vector pulse width modulation technology, control the inverter output voltage vector, and achieve precise adjustment of current and torque to drive the flat wire permanent magnet synchronous hub motor.

[0152] Alternatively, a high-voltage, low-loss algorithm can be used to address the low-loss requirements under high-load scenarios. This algorithm reduces copper losses by increasing the voltage. The specific logic is as follows:

[0153] When the torque T > 10.5 N·m and the gyroscope detects a tilt angle ≤ 5°, high-voltage control is activated.

[0154] The target voltage is calculated using the following formula:

[0155]

[0156] U bat This represents the battery's rated voltage, with a coefficient of 0.02 representing the optimized value obtained from actual measurements. Utarget The target driving voltage is T, and the torque is T. rated It is the rated torque of the motor.

[0157] Example: U bat When the voltage is 72V and the torque is T=12N·m (80% of rated torque), U target =72×{1+0.02×[(12-15) / (15)]}=72×0.996≈71.7V (fine-tuning); When T=15N·m (100% rated torque), U target =72×(1+0.02×0)=72V; When T=18N·m (120% rated torque, short-time peak value), U target =72×1.004≈72.3V.

[0158] Copper loss optimization effect: By increasing the voltage, the stator current I decreased from 18A (when U=72V) to 17.8A (when U=72.3V), the copper loss was reduced by about 2.2%, and the efficiency of the flat wire motor under high load was improved by 3%-5%.

[0159] According to a second aspect of the present invention, a long-range control device for an electric two-wheeled vehicle is provided, comprising:

[0160] The main controller and the memory connected to the main controller;

[0161] The memory stores program instructions;

[0162] The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0163] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0164] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0165] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0166] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0167] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0168] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0169] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0170] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0171] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A long-range control method for electric two-wheeled vehicles, characterized in that, include: Acquire real-time vehicle status data for electric two-wheelers, including: vehicle speed data, vehicle location data, user operation data, motor operation data, and slope. Based on the real-time vehicle condition data, key features are extracted; the key features include vehicle speed data, slope, vehicle positioning data, number of starts and stops, throttle opening fluctuation, motor load fluctuation, braking frequency, motor current, and throttle opening. Based on the aforementioned key features and according to the preset priority of application scenarios, the current application scenario of the electric two-wheeler is identified; Based on the identified application scenario, energy recovery strategies with different intensity ranges are activated, where the intensity range corresponds to the application scenario; Based on the aforementioned key features and according to the preset priority of application scenarios, the current application scenario of the electric two-wheeler is identified, including: Based on the gradient, motor current, vehicle speed data, and speed control lever opening, determine whether the current application scenario of the electric two-wheeler is a continuous uphill road condition. If the continuous uphill road conditions are not met, the current application scenario of the electric two-wheeler is determined based on the gradient, motor current, vehicle speed data and braking frequency. If the long downhill road conditions are not met, the current application scenario of the electric two-wheeler is determined based on the vehicle speed data, number of starts and stops, fluctuation of the speed control lever opening, and braking frequency. If the urban congested road conditions are not met, the current application scenario of the electric two-wheeler is determined to be a flat road cruising condition based on vehicle speed data, motor load fluctuation, slope, speed control lever opening fluctuation, vehicle positioning data and positioning prediction conditions. The positioning prediction conditions are as follows: based on the current position in the vehicle positioning data, calculate the slope standard deviation of the path 1km ahead from the map database; based on the current vehicle speed and direction, calculate the path the vehicle will take within 30 seconds; if the slope standard deviation is ≤1°, and it is determined from the map database that the path does not pass through an intersection, then the positioning prediction conditions are met.

2. The method according to claim 1, characterized in that, Based on the real-time vehicle status data, key features are extracted, including: Based on vehicle speed data, the number of starts and stops is calculated using the first sliding time window; Based on user operation data, the fluctuation of the speed control handle opening is calculated using the second sliding time window. Based on the motor operating data, the motor load fluctuation is calculated using the second sliding time window. Based on user operation data, the braking frequency is calculated using the third sliding time window; The vehicle speed data, gradient, vehicle positioning data, number of starts and stops, speed control lever opening fluctuation, motor load fluctuation, braking frequency, motor current, and speed control lever opening are integrated into key features.

3. The method according to claim 2, characterized in that, Based on the preset priority of application scenarios, identify the current application scenarios of the electric two-wheeler, including: If the slope is ≥5°, the motor current is ≥0.7×rated current, the vehicle speed is ≤25km / h, and the speed control handle opening is ≥60%, then the current application scenario for electric two-wheelers is continuous uphill road conditions. If the continuous uphill road condition is not met, then the following judgment is made: If the slope is ≤-3°, the motor current is ≤0.1×rated current, the vehicle speed is ≥15km / h, and the braking frequency is ≤2 times, then the current application scenario for electric two-wheelers is long downhill road conditions. If the long downhill road condition is not met, then the following judgment is made: If the vehicle speed is ≤15km / h, the number of start-stop times is ≥1, the speed control handle opening fluctuation is >50%, and the braking frequency is ≥1, then the current application scenario for electric two-wheelers is urban congested road conditions. If the urban traffic congestion conditions are not met, the following judgment will be made: If the vehicle speed is ∈ [20,45] km / h, the motor load fluctuation is ≤10%, the slope is ≤3°, the speed control handle opening fluctuation is ≤15%, and the positioning prediction conditions are met, then the current application scenario of the electric two-wheeler is flat road cruising.

4. The method according to claim 3, characterized in that, Also includes: After each preset time period, the application scenario of the electric two-wheeler is determined. If the application scenario is consistent in two consecutive determinations, the application scenario of the electric two-wheeler is switched to the determined application scenario. When the application scenario of the electric two-wheeler changes, the intensity of the energy recovery strategy is linearly changed within a preset time.

5. The method according to claim 1, characterized in that, Also includes: Obtain battery SOC and battery temperature; If the battery SOC > 95%, reduce the intensity of the current energy recovery strategy by 50%. If the battery temperature is >60℃, or if real-time vehicle condition data of the electric two-wheeler cannot be obtained, the energy recovery strategy will be disabled.

6. The method according to claim 3, characterized in that, Also includes: Obtain the mode switching command sent by the user, and switch the operating mode of the electric two-wheeler to super running mode, balanced mode or extreme speed mode according to the mode switching command; In Super Run mode, the speed of the electric two-wheeler is limited to below the preset maximum speed, and the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to the highest value within the intensity range. In balanced mode, the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to the preset median range within the intensity range; In high-speed mode, the intensity of the energy recovery strategy corresponding to the current application scenario is adjusted to a preset low range within the intensity range.

7. The method according to claim 6, characterized in that, Also includes: When the electric two-wheeler is cruising on a flat road, if the vehicle speed is between 30-35 km / h for more than 60 seconds, or if the speed control lever is open at 40%-60% for more than 60 seconds, then the electric two-wheeler's operating mode will be switched to Super Run mode. When an electric two-wheeler is on any road condition, if the speed control lever opening is greater than 90% for more than 1 second, or if the electric two-wheeler's acceleration is greater than 1.5 m / s², the following conditions will be met: 2 Then the electric two-wheeler's operating mode will be switched to high-speed mode; When the electric two-wheeler is on a continuous uphill road, if the slope is greater than 8% for more than 10 seconds, or the motor current is greater than 15A for more than 30 seconds, the operating mode of the electric two-wheeler will be switched to Super Run mode. When an electric two-wheeler is in congested urban traffic, if the duration of the speed being less than 15 km / h is greater than 10 seconds, or the frequency of starting and stopping is greater than 3 times per minute, the operating mode of the electric two-wheeler will be switched to balanced mode.

8. The method according to claim 1, characterized in that, Also includes: The real-time vehicle condition data and the vehicle condition data within a preset time period are input into a threshold-based lightweight decision tree model for trigger scenario prediction. Adjust the intensity of the energy recovery strategy based on the predicted triggering scenarios; If the triggering scenario does not occur within the preset time, the energy recovery strategy will be restored to its original strength.

9. The method according to claim 1, characterized in that, Also includes: Based on the acquired gyroscope attitude data and motor operation data, predict the changes in motor operating conditions within the next 5 control cycles; The objective function is to maximize motor efficiency, and current, voltage, and temperature constraints are set. Using the objective function and changes in motor operating conditions, the optimal driving parameters are solved through a quadratic programming algorithm; The optimal driving parameters are converted into motor driving signals using space vector pulse width modulation technology, which control the inverter output voltage vector to drive the flat wire permanent magnet synchronous hub motor.

10. A long-range control device for electric two-wheeled vehicles, characterized in that, include: The main controller, and the memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory to perform the method as described in any one of claims 1 to 9.

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