Personalized Range Recommendation Methods and Systems for Electric Motorcycle Users

Abstract

This invention provides a method and system for personalized range recommendation for electric motorcycle users, relating to the field of data processing technology. The method includes: Step 1, acquiring basic parameters of the electric motorcycle, including battery capacity, motor power, and average energy consumption data under standard test conditions; Step 2, based on the basic parameters, collecting the user's actual driving data in real time, including road condition type, gradient change, speed fluctuation, start-stop frequency, acceleration or braking behavior, and real-time coordinates of the battery pack center position, motor mounting position, and vehicle center of gravity position detected by onboard sensors. This invention, by integrating vehicle basic parameters, real-time driving data, and dynamic spatial relationship analysis, accurately generates personalized range recommendation values ​​and provides real-time feedback, effectively alleviating user range anxiety and improving driving experience and vehicle energy efficiency optimization capabilities.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for recommending personalized range for electric motorcycle users. Background Technology

[0002] Currently, the estimated range of electric motorcycles is generally based on average energy consumption data under standard testing conditions, which makes it difficult to accurately reflect the range under actual user scenarios. For example, user A frequently starts, stops, accelerates, and overtakes during daily commutes, while user B drives with a more stable style. However, traditional estimation methods use the same set of standard data to calculate the range for both of them, completely ignoring this difference in driving behavior. Furthermore, traditional methods neglect factors such as vehicle dynamics and complex road conditions, leading to a large discrepancy between predicted and actual range, exacerbating users' "range anxiety."

[0003] Existing technologies lack effective comprehensive consideration and real-time correction mechanisms when facing differences in user driving habits, changes in the dynamic spatial relationships of vehicle components, and environmental factors such as road slope and speed fluctuations. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method and system for personalized range recommendation for electric motorcycle users, so as to achieve more accurate range prediction and provide users with personalized and real-time range recommendations.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a method for recommending personalized range for electric motorcycle users, the method comprising:

[0007] Step 1: Obtain the basic parameters of the electric motorcycle, including battery capacity, motor power, and average energy consumption data under standard test conditions;

[0008] Step 2: Based on the basic parameters, collect the user's actual driving data in real time. The actual driving data includes road condition type, slope change, driving speed fluctuation, start-stop frequency, acceleration or braking behavior, and real-time coordinates of the battery pack center position, motor installation position and vehicle center of gravity position of the electric motorcycle detected by on-board sensors.

[0009] Step 3: Analyze the dynamic spatial relationship between the battery pack center position, motor mounting position and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combine the acceleration or braking frequency in user driving behavior and the slope change amplitude in environmental energy consumption influencing factors to generate dynamic energy consumption correction coefficient.

[0010] Step 4: Combine the average energy consumption data with the dynamic energy consumption correction coefficient to obtain the user's personalized energy consumption benchmark value.

[0011] Step 5: Based on the personalized energy consumption baseline value and battery capacity, dynamically generate a personalized range recommendation value for the user's current usage scenario, and feed it back to the user in real time through the vehicle terminal.

[0012] Furthermore, the dynamic spatial relationship between the battery pack center position, motor mounting position, and vehicle center of gravity position is analyzed, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in user driving behavior and the slope variation amplitude among environmental energy consumption influencing factors, dynamic energy consumption correction coefficients are generated, including:

[0013] Based on the real-time coordinates of the battery pack center position, motor mounting position, and vehicle center of gravity position, the first real-time displacement vector between the battery pack center position and the motor mounting position, the second real-time displacement vector between the motor mounting position and the vehicle center of gravity position, and the third real-time displacement vector between the vehicle center of gravity position and the battery pack center position are calculated respectively.

[0014] Based on the real-time change trends of the first, second, and third real-time displacement vectors, the displacement fluctuation amplitudes of the battery pack center position, motor mounting position, and vehicle center of gravity position within a preset time window are calculated, and distribution stability parameters are generated.

[0015] Based on the distribution stability parameter, combined with the acceleration or braking frequency in user driving behavior, the first influence weight of driving behavior on energy consumption is quantified, and combined with the slope change amplitude in environmental energy consumption influencing factors, the second influence weight of slope on energy consumption is quantified, thus obtaining the quantified driving behavior influence weight and slope additional weight.

[0016] The displacement fluctuation amplitude, distribution stability parameters, quantified driving behavior influence weights, and slope additional weights are integrated to generate a dynamic energy consumption correction coefficient.

[0017] Furthermore, based on the real-time changing trends of the first, second, and third real-time displacement vectors, the displacement fluctuation amplitudes of the battery pack center position, motor mounting position, and vehicle center of gravity position within a preset time window are calculated, generating distribution stability parameters, including:

[0018] Based on the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, the displacement of each displacement vector at each sampling point within a preset time window is extracted, and the maximum displacement, minimum displacement, and average displacement within the time window are calculated.

[0019] For each displacement vector, the displacement range within the time window is calculated based on the absolute difference between the maximum and minimum displacements. The absolute deviation between the displacement at each sampling point and the benchmark value is calculated using the average displacement as the benchmark value. The deviation magnitude is obtained by averaging the absolute deviations of all sampling points within the time window.

[0020] The displacement range and deviation amplitude are weighted and summed according to a preset ratio to generate the comprehensive displacement fluctuation amplitude of each displacement vector.

[0021] The combined displacement fluctuation amplitude of the three displacement vectors is normalized to generate distributed stability parameters that characterize the dynamic stability of the vehicle.

[0022] Furthermore, the average energy consumption data is integrated with the dynamic energy consumption correction factor to obtain a user-personalized energy consumption benchmark value, including:

[0023] Based on the distributed stability parameters, the first correction ratio of vehicle dynamic stability to energy consumption is determined through a preset stability correction mapping table.

[0024] Based on the first correction ratio, and combined with the influence weight of driving behavior and the additional weight of slope, the second correction ratio of driving behavior on energy consumption and the third correction ratio of slope on energy consumption are quantified through the driving behavior correction mapping table and the slope correction mapping table, respectively.

[0025] The first, second, and third correction ratios are superimposed on the average energy consumption data under the standard test environment to generate a user-personalized energy consumption benchmark value.

[0026] Furthermore, based on the first correction ratio, and combining the influence weight of driving behavior and the additional weight of slope, the second correction ratio of driving behavior on energy consumption and the third correction ratio of slope on energy consumption are quantified through the driving behavior correction mapping table and the slope correction mapping table, respectively, including:

[0027] Based on the acceleration or braking frequency in the user's driving behavior, the correction interval to which the acceleration or braking frequency belongs is determined through a preset driving behavior correction mapping table, and the corresponding second correction ratio is extracted; the driving behavior correction mapping table defines a linear correspondence between different acceleration or braking frequency ranges and correction ratios.

[0028] Based on the slope variation range among the environmental energy consumption influencing factors, a preset slope correction mapping table is used to determine the correction interval to which the slope variation range belongs, and the corresponding third correction ratio is extracted; the slope correction mapping table defines a nonlinear correspondence between different slope variation ranges and correction ratios.

[0029] Furthermore, based on personalized energy consumption baselines and battery capacity, a personalized recommended driving range for the user's current usage scenario is dynamically generated and fed back to the user in real time via the in-vehicle terminal, including:

[0030] Based on the user's personalized energy consumption benchmark, the theoretical energy consumption per kilometer is calculated, where the theoretical energy consumption is the ratio of the personalized energy consumption benchmark to the standard test mileage.

[0031] The initial theoretical driving range is generated based on the ratio of the user's current battery capacity to the theoretical energy consumption per kilometer.

[0032] The system can acquire the slope change range, speed fluctuation and start-stop frequency in the current driving scenario in real time, and make real-time corrections to the initial theoretical driving range through preset dynamic adjustment rules to generate a personalized driving range recommendation value for the user in the current scenario.

[0033] The vehicle's onboard terminal displays and provides users with personalized recommended driving range values ​​in real time.

[0034] Furthermore, it acquires real-time data on gradient changes, speed fluctuations, and start-stop frequency in the current driving scenario, and uses preset dynamic adjustment rules to correct the initial theoretical driving range in real time, generating a personalized driving range recommendation value for the user's current scenario, including:

[0035] Based on the gradient change range in the current driving scenario, a gradient reduction coefficient corresponding to the gradient change range is determined using a preset gradient reduction mapping table; the gradient reduction mapping table defines the correspondence between different gradient ranges and reduction ratios. Based on the driving speed fluctuations in the current driving scenario, a speed compensation coefficient corresponding to the driving speed fluctuations is determined using a preset speed compensation mapping table; the speed compensation mapping table defines the correspondence between the standard deviation range of speed fluctuations and the compensation ratio. Based on the start-stop frequency in the current driving scenario, a start-stop loss coefficient corresponding to the start-stop frequency is determined using a preset start-stop loss mapping table; the start-stop loss mapping table defines the correspondence between the range of start-stop frequency per unit time and the loss ratio.

[0036] The gradient reduction factor, speed compensation factor, and start-stop loss factor are superimposed on the initial theoretical range to generate a personalized range recommendation value for the user's current scenario.

[0037] Secondly, the personalized range recommendation system for electric motorcycle users includes:

[0038] The basic parameter acquisition module is used to acquire the battery capacity, motor power, and average energy consumption data of the electric motorcycle under standard test conditions.

[0039] The driving data acquisition module is used to collect real-time driving data based on basic parameters, such as the user's driving road conditions, slope changes, driving speed fluctuations, start-stop frequency, acceleration or braking behavior, as well as to detect the real-time coordinates of the battery pack center position, motor installation position and vehicle center of gravity position of the electric motorcycle through on-board sensors.

[0040] The dynamic relationship analysis module is used to analyze the dynamic spatial relationship changes of the battery pack center position, motor mounting position and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in user driving behavior and the slope change amplitude in environmental energy consumption influencing factors, dynamic energy consumption correction coefficients are generated.

[0041] The energy consumption benchmark generation module is used to integrate average energy consumption data with dynamic energy consumption correction coefficients to obtain personalized energy consumption benchmark values ​​for users.

[0042] The driving range recommendation module is used to dynamically generate a personalized driving range recommendation value for the user's current usage scenario based on the personalized energy consumption benchmark value and battery capacity, and then feed it back to the user in real time through the vehicle terminal.

[0043] Thirdly, a computing device includes:

[0044] One or more processors;

[0045] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0046] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0047] The above-described solution of the present invention has at least the following beneficial effects:

[0048] By combining basic vehicle parameters and real-time driving data, including factors such as driving behavior, road conditions, and slope, the system analyzes the dynamic spatial relationship between the battery pack, motor, and vehicle center of gravity to construct a dynamic energy consumption correction coefficient. This allows for the accurate calculation of personalized energy consumption benchmark values, making the recommended driving range closer to real-world conditions, effectively alleviating users' concerns about battery life, and facilitating reasonable trip planning.

[0049] By collecting data in real time through onboard sensors, the system dynamically generates recommended driving range values ​​and promptly feeds them back to the vehicle's terminal. Users can obtain accurate range information at any time while driving, enhancing their sense of control over the vehicle's status. Simultaneously, this real-time feedback mechanism can encourage users to adjust their driving habits, such as adopting more energy-efficient driving methods when the remaining range is low, indirectly improving energy efficiency.

[0050] The vast amount of multi-dimensional data collected provides a wealth of resources for optimizing electric motorcycle technology. Manufacturers can improve battery management systems and optimize motor design based on personalized user energy consumption data and vehicle dynamic characteristics analysis, thereby enhancing overall vehicle performance and energy efficiency. For example, by analyzing the impact of component dynamic spatial relationships on energy consumption, vehicle layout design can be optimized to reduce unnecessary energy losses. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating the personalized range recommendation method for electric motorcycle users provided in an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of a personalized range recommendation system for electric motorcycle users provided in an embodiment of the present invention. Detailed Implementation

[0053] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0054] like Figure 1 As shown, embodiments of the present invention propose a method for recommending personalized range for electric motorcycle users, the method comprising the following steps:

[0055] Step 1: Obtain the basic parameters of the electric motorcycle, including battery capacity, motor power, and average energy consumption data under standard test conditions;

[0056] Step 2: Based on the basic parameters, collect the user's actual driving data in real time. The actual driving data includes road condition type, slope change, driving speed fluctuation, start-stop frequency, acceleration or braking behavior, and real-time coordinates of the battery pack center position, motor installation position and vehicle center of gravity position of the electric motorcycle detected by on-board sensors.

[0057] Step 3: Analyze the dynamic spatial relationship between the battery pack center position, motor mounting position and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combine the acceleration or braking frequency in user driving behavior and the slope change amplitude in environmental energy consumption influencing factors to generate dynamic energy consumption correction coefficient.

[0058] Step 4: Combine the average energy consumption data with the dynamic energy consumption correction coefficient to obtain the user's personalized energy consumption benchmark value.

[0059] Step 5: Based on the personalized energy consumption baseline value and battery capacity, dynamically generate a personalized range recommendation value for the user's current usage scenario, and feed it back to the user in real time through the vehicle terminal.

[0060] In this embodiment of the invention, by collecting user driving behavior data in real time (such as acceleration or braking frequency, start-stop mode), and combining it with vehicle dynamic spatial relationship analysis, accurate modeling of different user driving habits is achieved. This transforms range prediction from a "standardized uniformity" to a "person-specific strategy," improving the consistency between prediction results and actual use. Based on real-time monitoring of the dynamic changes in the battery pack, motor, and center of gravity position by onboard sensors, subtle energy consumption characteristics during vehicle operation (such as component vibration and center of gravity shift) are captured. Furthermore, the correction coefficient is adjusted in real time based on environmental factors such as slope and road conditions to ensure that the range prediction always reflects the current real-world scenario.

[0061] By combining vehicle structural dynamics parameters (relative displacement of components, distribution stability) with traditional energy consumption data, this approach overcomes the limitations of existing technologies that rely solely on single-dimensional analysis based on driving behavior or environmental factors, forming a more comprehensive and in-depth system of energy consumption influencing factors. Through real-time updates of recommended driving range values ​​via the in-vehicle terminal, users can intuitively obtain range information matching their current driving status, effectively alleviating "range anxiety." Simultaneously, it continuously learns user behavior patterns, gradually improving prediction accuracy. It performs specific optimizations for complex road conditions (such as urban roads with frequent starts and stops, and mountain roads with continuous inclines) and special driving styles (aggressive / stable driving), maintaining high prediction accuracy even in extreme scenarios. By analyzing the correlation between dynamic spatial relationships and energy consumption, it can indirectly provide data support for vehicle design optimization (such as evaluating the rationality of component layout). Furthermore, users can proactively adjust their driving habits to improve energy efficiency based on feedback on range change trends, forming a closed loop of "prediction-feedback-optimization."

[0062] In a preferred embodiment of the present invention, step 1 above involves acquiring the basic parameters of the electric motorcycle, including battery capacity, motor power, and average energy consumption data under standard test conditions; step 2 above involves, based on the basic parameters, real-time collection of the user's actual driving data, including road condition type, gradient change, speed fluctuation, start-stop frequency, acceleration or braking behavior, and real-time detection of the battery pack center position, motor mounting position, and vehicle center of gravity position of the electric motorcycle via onboard sensors, which may include:

[0063] In this embodiment of the invention, when the vehicle rolls off the production line, the battery manufacturer writes the nominal capacity (e.g., 100Ah) to the vehicle's ECU (electronic control unit) via an encryption chip, and simultaneously records information such as the battery batch and chemical system (e.g., ternary lithium / lithium iron phosphate).

[0064] Real-time capacity monitoring:

[0065] BMS Hardware: The battery management system has a built-in fuel gauge chip (such as the TIBQ series) that calculates the remaining capacity by combining coulomb counting (cumulative charge and discharge current) with ampere-hour integration and compensates for self-discharge losses.

[0066] Aging Correction: The ECU maintains the battery health (SOH) curve and automatically reduces the nominal capacity based on the number of charge-discharge cycles (statistics via CAN bus) and historical temperature data. For example, after 500 cycles, the SOH drops to 85%, and the actual usable capacity is updated to 85Ah.

[0067] On the first full charge each month, the ECU triggers a "deep calibration mode": after charging until the BMS reports 100% SOC, it continues trickle charging for 30 minutes, recording the actual charge amount to correct the capacity model. The motor nameplate uses an NFC tag or QR code. The vehicle system reads information such as rated power (e.g., 3kW), peak power (e.g., 5kW), and highest efficiency speed (e.g., 3000r / min) via a card reader and stores it in the vehicle configuration table. The motor controller has built-in current sensors (Hall effect) and voltage sensors, collecting bus current (e.g., 0-50A) and voltage (e.g., 48V platform) at a 100Hz frequency.

[0068] Instantaneous power = bus voltage × bus current × controller efficiency (default 95%). The ECU calculates the average value once per second, distinguishing between drive power (positive value) and energy recovery power (negative value). When the GPS detects an uphill section (slope > 5°), the ECU automatically retrieves the motor overload characteristic curve, allows short-term peak power output, and records the overload duration to assess the impact of motor temperature rise.

[0069] Laboratory testing procedures:

[0070] Testing equipment: Chassis dynamometer to simulate different road surface resistances, and environmental chamber to control temperature (25℃±2℃) and humidity (60%±5%).

[0071] Urban driving conditions: Simulating the GB / T24157-2017 standard, including idling, low speed (20km / h), and medium speed (40km / h) cycles, recording an average energy consumption of 15Wh / km.

[0072] High-speed operation: constant speed of 60km / h, energy consumption recorded as 12Wh / km (low wind resistance).

[0073] The database establishes a "working condition-energy consumption" mapping table, with fields including: working condition type (city / highway / mountain road), average speed, average acceleration, and energy consumption value, supporting quick retrieval of baseline data according to the current driving scenario.

[0074] Main sensor: High-precision GNSS module (such as u-blox ZED-F9P), providing centimeter-level positioning accuracy, with a built-in electronic compass to distinguish driving direction.

[0075] Auxiliary sensors: Forward-facing camera (5 megapixels, 30fps), equipped with MobileNetV3 model to recognize traffic signs in real time (such as speed limits and congestion warnings).

[0076] Map pre-classification: After GNSS obtains latitude and longitude, it matches high-precision maps through the vehicle navigation engine and marks road types (such as "urban expressway" and "rural mountain road").

[0077] Visual verification: When the camera detects that the distance between vehicles ahead is less than 5 meters and lasts for more than 1 minute, a "congestion" marker is triggered, overriding the map classification results.

[0078] Data output: Update road condition labels (such as "city congestion - low speed - frequent stops and starts") every second and store them in a circular buffer.

[0079] Attitude calculation process:

[0080] Accelerometer: Collects acceleration along three axes: X (longitudinal), Y (lateral), and Z (vertical). Vibration noise is removed by low-pass filtering (cutoff frequency 10Hz), and the vertical acceleration component (Z-axis) is extracted to calculate the slope sine value.

[0081] Gyroscope: measures pitch rate, integrates to obtain real-time pitch angle, and combines with vehicle wheelbase to correct slope calculation (e.g., wheelbase 1.5 meters, pitch angle 1° corresponds to slope ≈ 1.745%).

[0082] The system synchronizes GNSS altitude data every minute (accuracy ±1 meter), calculates the ratio of altitude difference between adjacent moments to travel distance, and corrects sensor gradient calculation errors. For example, if the altitude increases by 5 meters after traveling 100 meters, the gradient is determined to be 5%, and a weighted average is taken with the sensor calculation value (sensor weight 70%, altitude weight 30%).

[0083] The speed sensor uses a magnetoelectric encoder (mounted on the hub motor), which outputs 1024 pulses per revolution. The ECU calculates the instantaneous speed by measuring the pulse interval (accuracy ±0.1km / h), with a sampling frequency of 100Hz.

[0084] Sliding window analysis:

[0085] Using a 60-second window, the data within the window is updated every second, and the average speed and speed standard deviation are calculated. The speed standard deviation reflects the degree of fluctuation. When the speed standard deviation is greater than 5 km / h, it is judged as "severe fluctuation". The number of rapid accelerations is the number of events with a speed increase of more than 10 km / h and a duration of less than 2 seconds. When the speed standard deviation is greater than 8 km / h and the number of rapid accelerations is greater than 3 times / minute, it is marked as "aggressive driving - high-speed fluctuation" scenario.

[0086] Start-stop frequency:

[0087] Effective parking: Speed ​​≤ 0km / h and duration ≥ 3 seconds (excluding temporary coasting) triggers a parking event;

[0088] Effective start: The start event is triggered when the speed increases from 0 km / h to ≥5 km / h and the acceleration is ≥0.3 m / s².

[0089] Within a 10-minute statistical period:

[0090] Start-stop frequency ≤ 3 times: Marked as "Smooth Driving";

[0091] Start-stop frequency 4-6 times: Marked as "Regular urban traffic conditions";

[0092] Start-stop frequency ≥ 7 times: Mark as "severe congestion".

[0093] Acceleration or braking behavior:

[0094] Light acceleration: 0.5 m / s² ≤ acceleration < 1.0 m / s²;

[0095] Rapid acceleration: acceleration ≥ 1.0 m / s²;

[0096] Light braking: -1.0 m / s² < deceleration ≤ -0.5 m / s²;

[0097] Emergency braking: deceleration ≤ -1.0 m / s².

[0098] Each time an acceleration or braking event is triggered, record: event type (mild / urgent); duration (e.g., rapid acceleration lasts 2.5 seconds); energy change, i.e., additional energy consumption calculated by integrating the motor power curve (e.g., rapid acceleration consumes 50Wh).

[0099] Vehicle component location coordinates:

[0100] Battery pack center: Install a MEMS accelerometer (such as ADXL345) and an ultrasonic displacement sensor. The former monitors vibration acceleration, and the latter measures the relative displacement between the battery pack and the vehicle frame (accuracy ±0.1mm).

[0101] Motor mounting position: A laser displacement sensor is fixed on the motor base to measure the horizontal / vertical displacement of the motor and the frame in real time (dynamic range ±10mm).

[0102] Vehicle center of gravity position: The real-time center of gravity coordinates are calculated by using an onboard IMU (such as MPU9250) combined with a vehicle mass distribution model (preset battery 40kg, motor 15kg, frame 25kg) (the origin of the coordinate system is set to the front axle center).

[0103] The vehicle automatically enters "calibration mode" upon first start each week:

[0104] Keep the vehicle stationary for 5 minutes and record the initial values ​​of each sensor (battery displacement 0mm, motor displacement 0mm, center of gravity coordinates (0.8m, 0.3m)). After driving 1 kilometer, stop the vehicle and compare the dynamic data with the initial values ​​to correct the sensor offset (such as displacement error caused by temperature drift).

[0105] All sensors are connected to the vehicle gateway via a CAN bus. The gateway has a built-in GPS timing module that broadcasts a UTC timestamp to the sensors every 10 seconds to ensure that the data timestamp error is less than 1ms.

[0106] Pre-processing line:

[0107] Noise reduction: Apply a 5-point moving average filter to acceleration, displacement and other signals to eliminate high-frequency noise (such as road bumps).

[0108] Filling gaps: If data from a sensor is lost (e.g., due to GNSS signal obstruction), the HoldLastValue method is used to fill the gaps until the signal is restored.

[0109] Normalization: Mapping data of different dimensions (such as slope, speed standard deviation) to the interval [0, 1].

[0110] In a preferred embodiment of the present invention, step 3 above analyzes the dynamic spatial relationship changes of the battery pack center position, motor mounting position, and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in user driving behavior and the slope change amplitude among environmental energy consumption influencing factors, a dynamic energy consumption correction coefficient is generated, which may include:

[0111] Step 300: Based on the real-time coordinates of the battery pack center position, the motor mounting position, and the vehicle center of gravity position, calculate the first real-time displacement vector between the battery pack center position and the motor mounting position, the second real-time displacement vector between the motor mounting position and the vehicle center of gravity position, and the third real-time displacement vector between the vehicle center of gravity position and the battery pack center position.

[0112] Step 301: Based on the real-time change trends of the first, second, and third real-time displacement vectors, calculate the displacement fluctuation amplitude of the battery pack center position, motor mounting position, and vehicle center of gravity position within a preset time window, and generate distribution stability parameters, specifically including:

[0113] Step 3010: Based on the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, extract the displacement of each sampling point within a preset time window for each displacement vector, and calculate the maximum displacement, minimum displacement, and average displacement within the time window.

[0114] Step 3011: For each displacement vector, calculate the displacement range within the time window based on the absolute difference between the maximum and minimum displacements, and calculate the absolute deviation between the displacement at each sampling point and the benchmark value using the average displacement as the benchmark value; take the average of the absolute deviations of all sampling points within the time window to obtain the deviation magnitude.

[0115] Step 3012: The displacement range and deviation amplitude are weighted and summed according to a preset ratio to generate the comprehensive displacement fluctuation amplitude of each displacement vector.

[0116] Step 3013: Normalize the combined displacement fluctuation amplitude of the three displacement vectors to generate distributed stability parameters characterizing the dynamic stability of the vehicle.

[0117] Step 302: Based on the distribution stability parameter and combined with the acceleration or braking frequency in the user's driving behavior, quantify the first influence weight of driving behavior on energy consumption, and combine with the slope change amplitude in the environmental energy consumption influencing factors to quantify the second influence weight of slope on energy consumption, so as to obtain the quantified driving behavior influence weight and slope additional weight.

[0118] Step 303: The displacement fluctuation amplitude, distribution stability parameters, quantified driving behavior influence weights and slope additional weights are fused to generate a dynamic energy consumption correction coefficient.

[0119] In this embodiment of the invention, when the electric motorcycle is in motion, high-precision sensors (such as laser displacement sensors and inertial measurement units) installed at the center of the battery pack, the motor mounting position, and the vehicle's center of gravity continuously collect data. These sensors act like the vehicle's "electronic eyes," constantly monitoring the position of components.

[0120] Using the chassis as a reference coordinate system is like setting up a "fixed room" for the vehicle. The position of all components is determined relative to this "room". The coordinate data collected by the sensors is uniformly transformed into this chassis coordinate system.

[0121] In specific calculations, for example, calculating the first real-time displacement vector between the center position of the battery pack and the motor mounting position will use the real-time coordinates of the center position of the battery pack in the vehicle frame coordinate system. Subtract the real-time coordinates of the motor installation location in the same coordinate system. The obtained three-dimensional coordinate difference ( The first real-time displacement vector indicates the distance the battery pack center has moved relative to the motor's mounting position in the front-back, left-right, and up-down directions. Similarly, through a similar subtraction operation, the second real-time displacement vector (the relative displacement between the motor and the center of gravity) and the third real-time displacement vector (the relative displacement between the center of gravity and the battery pack) are obtained. These displacement vectors change in real time as the vehicle bumps, turns, accelerates, and decelerates.

[0122] Step 3010: First, set a preset time window, say 1 minute, and observe the changes in the displacement vector during this time. During this 1 minute, the sensor collects data once per second, resulting in 60 sampling points. For each real-time displacement vector, calculate its displacement at each sampling point. This displacement is the length of the displacement vector. Imagine it as a directional rope, and you want to measure its length. The specific calculation method uses the Pythagorean theorem, taking the square root of the sum of the squares of the components of the displacement vector on the three coordinate axes (i.e., the coordinate differences). For example, if the components of a displacement vector on the three coordinate axes are 3 cm, 4 cm, and 0 cm, then its displacement is... centimeter.

[0123] During the statistical process, the maximum, minimum, and average displacement of each displacement vector within one minute were carefully recorded. The maximum displacement was the largest value among the 60 sampling points, and the minimum displacement was the smallest value. The average displacement was calculated by summing the displacements of all 60 sampling points and then dividing by 60. These statistical values ​​allow us to clearly understand the maximum and minimum ranges of displacement vector changes within one minute, as well as the overall average level of change.

[0124] Step 3011: For each displacement vector, first calculate the displacement range. The displacement range is the absolute difference between the maximum and minimum displacement. For example, if the maximum displacement of a displacement vector is 8 cm and the minimum displacement is 2 cm, then the displacement range is |8-2|=6 cm. This value intuitively reflects the fluctuation range of the displacement within a 1-minute time window; the larger the value, the more drastic the displacement change.

[0125] Next, the average displacement is used as the baseline value. For each sampling point, the absolute deviation between its displacement and the average displacement is calculated. For example, if the displacement of a sampling point is 5 cm and the average displacement is 3 cm, then the absolute deviation is |5 cm|. 3 = 2 cm. Then, sum the absolute deviations of all 60 sampling points within the time window, and divide by 60. The result is the deviation magnitude. The deviation magnitude reflects the degree of dispersion of the displacement at each sampling point relative to the average level; the larger the deviation magnitude, the more unstable the fluctuation of the displacement.

[0126] Step 3012: Set a preset ratio for the displacement range and deviation amplitude, assuming the displacement range accounts for 40% and the deviation amplitude accounts for 60%. For each displacement vector, multiply its displacement range by 40% and its deviation amplitude by 60%, then add these two results together. The resulting value is the comprehensive displacement fluctuation amplitude of that displacement vector. The comprehensive displacement fluctuation amplitude calculated in this way considers both the range of displacement variation (displacement range) and the dispersion of displacement relative to the average level (deviation amplitude), thus reflecting the fluctuation characteristics of the displacement vector more comprehensively and accurately.

[0127] Step 3013: Normalize the combined displacement fluctuation amplitude of the three displacement vectors. Normalization is like "converting" values ​​of different magnitudes into a unified range of 0 to 1. Specifically, divide each combined displacement fluctuation amplitude by the maximum value among the three. For example, if the three combined displacement fluctuation amplitudes are 10, 8, and 6, and the maximum value is 10, then the normalized value of the first displacement vector is 10 ÷ 10 = 1, the second is 8 ÷ 10 = 0.8, and the third is 6 ÷ 10 = 0.6. Then, take a weighted average of these three normalized values. Assuming equal weights, this means adding the three normalized values ​​together and then dividing by 3. The result is the distribution stability parameter characterizing the vehicle's dynamic stability. The larger this parameter value, the more drastic the relative position changes of the battery pack, motor, and center of gravity during vehicle operation, and the worse the vehicle's dynamic stability; conversely, the smaller the value, the better the vehicle's dynamic stability.

[0128] Step 302, the analysis process for the first influence weight (driving behavior) is as follows:

[0129] Distributed stability parameters: reflect the degree of drastic change in the relative position of vehicle components (the larger the parameter value, the more frequent the vibration or displacement).

[0130] Acceleration or braking frequency: High-frequency acceleration or braking directly reflects the aggressiveness of driving behavior;

[0131] When the distribution stability parameter is higher than threshold A and the acceleration or braking frequency is higher than threshold B, driving behavior is determined to have a significant impact on energy consumption, with the first impact weight ranging from 60% to 80%.

[0132] When the distribution stability parameter is moderate (threshold) -Threshold A) and acceleration or braking frequency is moderate (threshold A) When threshold B is reached, the first influence weight ranges from 30% to 60%.

[0133] When both parameters and frequency are below 50% of the threshold, the impact is weak, and the weight of the first impact ranges from 10% to 30%.

[0134] The analysis process for the second influence weight (slope) is as follows:

[0135] Driving factor: The magnitude of gradient change reflects the terrain's demand on vehicle power output. A critical gradient value Y (unit: degrees) is set as the evaluation benchmark. This value is determined based on extensive real-world testing and is used to classify the levels of gradient's impact on energy consumption.

[0136] When the slope change is greater than Y degrees, the terrain is considered to have a significant impact on energy consumption, and the second influence weight ranges from 40% to 60%. For example, on a mountainous road with continuous uphill sections, vehicles need to maintain high power output, and a high weight can effectively reflect the impact of slope on energy consumption.

[0137] When the slope changes by a certain amount When the slope is between 0 and Y degrees, the terrain influence is considered to be moderate, with a weighting range of 20%-40%. This situation is common on undulating roads in urban and suburban areas. The slope has a certain impact on energy consumption, but it is not dominant.

[0138] When the slope change range is < When the slope is low, the impact of terrain on energy consumption is considered relatively small, with a weighting range of 10%-20%. For example, when driving on flat urban roads, the effect of slope on energy consumption can be relatively weakened.

[0139] Based on historical test data (covering different driving styles and terrain conditions), a parameter combination-weight value mapping relationship is established using a regression analysis algorithm. If the distribution stability parameter is high, the acceleration or braking frequency is high, and the slope change amplitude is low, the first weight is 70%, and the second weight is 15%. If the distribution stability parameter is medium, the acceleration or braking frequency is low, and the slope change amplitude is medium, the first weight is 40%, and the second weight is 35%.

[0140] Step 303, Dynamic energy consumption correction coefficient = Among them, the factor values ​​include:

[0141] Displacement fluctuation amplitude (comprehensive value): reflects the displacement stability of the vehicle during operation, with a weighting of 20%-30% (dynamically adjusted according to the scenario, for example, 25% for urban roads and 20% for highways).

[0142] Distribution stability parameter: reflects the vibration intensity of the component, with a weight of 25%-35% (35% for vibration-sensitive scenarios and 25% for stable scenarios).

[0143] Driving behavior influence weight (output of step 302): directly related to driving style, with a weight of 25%-35% (35% for aggressive driving and 25% for stable driving).

[0144] Slope weighting (output of step 302): reflects the difficulty of the terrain, with a weighting of 15%-25% (25% for mountain scenes and 15% for plains).

[0145] Suppose a certain scenario:

[0146] Displacement fluctuation amplitude = 0.8, corresponding to a weight of 25%;

[0147] Distribution stability parameter = 0.6, corresponding to a weight of 30%;

[0148] Driving behavior influences a weight of 70%, corresponding to a weight of 30%.

[0149] Slope additional weight = 50%, corresponding weight 20%;

[0150] The correction factor is: 0.8×25%+0.6×30%+70%×30%+50%×20%=0.69.

[0151] By calculating dynamic energy consumption correction coefficients, the impact of dynamic spatial relationships of vehicle components, driving behavior, and environmental factors on energy consumption can be comprehensively considered. Specifically, precise analysis of the relative displacement and distribution stability of components can capture energy consumption changes caused by vibration, center of gravity shifts, etc., during vehicle operation; quantifying the influence weights of driving behavior and slope allows for precise corrections for different driving styles and road conditions. The resulting dynamic energy consumption correction coefficients improve the accuracy of range prediction, making the predictions more closely match actual user scenarios, effectively alleviating range anxiety, and providing data support for vehicle energy consumption optimization and performance improvement.

[0152] In a preferred embodiment of the present invention, step 4 above, which fuses the average energy consumption data with the dynamic energy consumption correction coefficient to obtain a user-personalized energy consumption benchmark value, may include:

[0153] Step 400: Based on the distributed stability parameters, determine the first correction ratio of vehicle dynamic stability to energy consumption through a preset stability correction mapping table.

[0154] Step 401: Based on the first correction ratio, and combining the influence weight of driving behavior and the additional weight of slope, quantify the second correction ratio of driving behavior on energy consumption and the third correction ratio of slope on energy consumption using the driving behavior correction mapping table and the slope correction mapping table, respectively. Specifically, this includes:

[0155] Step 4010: Based on the acceleration or braking frequency in the user's driving behavior, determine the correction interval to which the acceleration or braking frequency belongs through a preset driving behavior correction mapping table, and extract the corresponding second correction ratio; the driving behavior correction mapping table defines a linear correspondence between different acceleration or braking frequency ranges and correction ratios.

[0156] Step 4011: Based on the slope change range among the environmental energy consumption influencing factors, determine the correction interval to which the slope change range belongs through a preset slope correction mapping table, and extract the corresponding third correction ratio; the slope correction mapping table defines a non-linear correspondence between different slope change ranges and correction ratios.

[0157] Step 402: The first correction ratio, the second correction ratio, and the third correction ratio are superimposed on the average energy consumption data under the standard test environment to generate a user-personalized energy consumption benchmark value.

[0158] In this embodiment of the invention, when the electric motorcycle is running, the system continuously monitors the relative position changes of the battery pack, motor, and vehicle center of gravity, and calculates a distribution stability parameter. This parameter ranges from 0 to 1; the closer the value is to 0, the more stable the relative positions of the components are during vehicle operation; the closer the value is to 1, the more drastic the relative displacement of the components, and the worse the vehicle's dynamic stability.

[0159] The system has a pre-built stability correction mapping table specifically designed to determine the correction ratios for different stability states. It divides the distributed stability parameters into multiple intervals, for example:

[0160] Interval 1 (Extremely High Stability): When the distributed stability parameter is in the range of 0-0.1, it indicates that the vehicle experiences almost no component displacement changes during driving and is in a very stable state. In this case, the mapping table specifies a first correction ratio of 3%. This means that under this ideal state, the impact of vehicle dynamic stability on energy consumption is minimal, only increasing energy consumption by 3% from the standard value.

[0161] Interval 2 (High Stability): If the distributed stability parameter is between 0.1 and 0.3, the vehicle stability is good, and the relative displacement of components is slight. The first correction ratio corresponding to this interval in the mapping table is 8%, meaning that vehicle dynamic stability will increase energy consumption by 8%.

[0162] Interval 3 (Medium Stability): When the distributed stability parameter is in the range of 0.3-0.6, the relative displacement of components is relatively significant during vehicle operation, and the stability is at a moderate level. In this case, the mapping table specifies a first correction ratio of 15%.

[0163] Interval 4 (Low Stability): If the distributed stability parameter is between 0.6 and 0.8, the displacement of vehicle components changes significantly, resulting in poor stability. The first correction ratio is 22%.

[0164] Interval 5 (extremely poor stability): When the distribution stability parameter exceeds 0.8, the relative positions of the vehicle's components change drastically during driving, resulting in an unstable state. The first correction ratio is set to 30%.

[0165] The system reads the current distribution stability parameter in real time, determines which interval it belongs to, and then finds the corresponding first correction ratio from the mapping table. For example, if the currently calculated distribution stability parameter is 0.5, it is determined to be in "interval three (medium stability)", and 15% is selected as the first correction ratio.

[0166] Step 4010: Continuously record the acceleration or braking frequency during the user's driving process, i.e., the number of times the vehicle accelerates or brakes per minute. To quantify the impact of driving behavior on energy consumption, the system has a preset driving behavior correction mapping table, which specifies in detail the linear correspondence between different acceleration or braking frequency ranges and correction ratios.

[0167] It divides the acceleration or braking frequency into the following intervals:

[0168] Zone 1 (Ultra-Smooth Driving): When the acceleration or braking frequency is less than or equal to 2 times per minute, it indicates that the user's driving style is extremely smooth, with very few acceleration or deceleration operations. In this case, the driving behavior correction mapping table specifies a corresponding second correction ratio of 10%. This means that a smooth driving style has a small impact on the increase in energy consumption, only increasing energy consumption by 10% from the standard value;

[0169] Zone Two (Smooth Driving): If the acceleration or braking frequency is between 3 and 5 times per minute, it is considered a normal and smooth driving style. The second correction ratio for this zone is 18%, meaning that this driving style will increase energy consumption by 18%.

[0170] Zone 3 (Normal Driving): When the acceleration or braking frequency is 6-8 times per minute, the driving style is relatively ordinary, with some acceleration and deceleration operations. In this case, the mapping table specifies a second correction ratio of 25%.

[0171] Zone 4 (Aggressive Driving): If the acceleration or braking frequency is between 9 and 12 times per minute, it indicates that the user is driving aggressively and frequently accelerating or decelerating. The corresponding second correction ratio for this zone is 32%.

[0172] Zone 5 (Extremely Aggressive Driving): When the frequency of acceleration or braking exceeds 12 times per minute, the driving behavior is extremely aggressive, with very frequent acceleration and deceleration operations. In this case, the second correction ratio is set to 40%.

[0173] The system will count the number of accelerations or brakings in the most recent minute in real time, determine the interval to which it belongs, and then extract the corresponding second correction ratio from the driving behavior correction mapping table. For example, if the system detects that the vehicle accelerates or brakes 7 times in a certain minute, then it belongs to "Interval 3 (normal driving)", and the system will extract 25% as the second correction ratio.

[0174] Step 4011: Real-time data on the slope change during vehicle operation is collected using onboard sensors. Since the effect of slope on energy consumption is not uniformly linear (energy consumption increases little at small slopes, but increases significantly at large slopes), a preset slope correction mapping table defines a non-linear correspondence between different slope change ranges and correction ratios.

[0175] This mapping table divides the slope variation range (in degrees) into multiple intervals:

[0176] Interval 1 (Nearly Flat): When the slope variation is between 0 and 2 degrees, the road surface is almost flat, and vehicle movement is largely unaffected by the slope. In this case, the slope correction mapping table specifies a third correction ratio of 5%, meaning that such a slight slope will only increase energy consumption by 5% from the standard value.

[0177] Interval Two (Slight Slope): If the slope variation is between 2 and 5 degrees, it is considered a light slope road section. The corresponding third correction ratio for this interval is 12%, meaning that a light slope will increase energy consumption by 12%.

[0178] Section 3 (Medium Gradient): When the gradient changes between 5 and 8 degrees, vehicles need to overcome a certain amount of gravity, which is considered a medium gradient section. In this case, the mapping table specifies a correction ratio of 22% for Section 3.

[0179] Section 4 (Steep Slope): If the slope change is between 8 and 12 degrees, the difficulty of driving increases significantly, and energy consumption rises sharply. The third correction ratio for this section is 35%.

[0180] Interval 5 (Extreme Gradient): When the gradient change exceeds 12 degrees, vehicle operation faces significant challenges and requires substantial energy consumption. In this case, the third correction ratio is set to 50%.

[0181] The system acquires real-time data on the current slope change, determines its corresponding interval, and then extracts the corresponding third correction ratio from the slope correction mapping table. For example, if the detected slope change of the current road segment is 9 degrees, it belongs to "Interval Four (Serious Slope)," and 35% is extracted as the third correction ratio.

[0182] Step 402: First, obtain the average energy consumption data under standard test conditions. This is the average electricity consumption per kilometer driven by the vehicle, obtained under ideal and stable conditions, for example, 18Wh / km (Wh represents watt-hours, a unit for measuring electrical energy). Adjust the average energy consumption data according to the first, second, and third correction ratios obtained in the previous three steps. The specific process is as follows:

[0183] Calculate the increase in energy consumption due to vehicle dynamic stability: Multiply the average energy consumption data by the first correction ratio. For example, if the average energy consumption data is 18Wh / km and the first correction ratio is 15%, then the increase in energy consumption due to vehicle dynamic stability = 18 × 15% = 2.7Wh / km.

[0184] Calculate the increase in energy consumption due to driving behavior: multiply the average energy consumption data by the second correction ratio. Assuming the second correction ratio is 25%, the increase in energy consumption due to driving behavior = 18 × 25% = 4.5 Wh / km.

[0185] Calculate the increase in energy consumption due to slope: multiply the average energy consumption data by the third correction ratio. If the third correction ratio is 35%, then the increase in energy consumption due to slope = 18 × 35% = 6.3 Wh / km.

[0186] Finally, the average energy consumption data is added to the three additional values ​​mentioned above to obtain the user's personalized energy consumption baseline value: Personalized energy consumption baseline value = 18 + 2.7 + 4.5 + 6.3 = 31.5 Wh / km. This value comprehensively considers vehicle dynamic stability, user driving behavior, and environmental slope factors, and can more accurately reflect the user's actual energy consumption level in the current driving scenario.

[0187] By fusing average energy consumption data with dynamic energy consumption correction coefficients, a personalized energy consumption benchmark value is generated for each user, achieving a high degree of accuracy and personalization in energy consumption assessment. Traditional energy consumption calculations are often based on standardized test data, failing to reflect the differences in vehicle status, user habits, and environmental factors during actual driving. This method, however, meticulously analyzes factors such as distribution stability, driving behavior, and gradient to provide exclusive energy consumption benchmark values ​​for different users and driving scenarios, making energy consumption assessments more closely aligned with real-world conditions.

[0188] Secondly, it improves the reliability of range prediction. Accurate personalized energy consumption benchmarks are the key foundation for calculating range. Based on this, the recommended range allows users to understand more clearly and accurately the vehicle's remaining driving capacity in the current usage scenario, effectively alleviating users' "range anxiety" and avoiding problems such as trip interruptions caused by misjudgment of range.

[0189] In a preferred embodiment of the present invention, step 5 above, which dynamically generates a personalized driving range recommendation value for the user's current usage scenario based on the personalized energy consumption benchmark value and battery capacity, and feeds it back to the user in real time through the vehicle terminal, may include:

[0190] Step 500: Calculate the theoretical energy consumption per kilometer based on the user's personalized energy consumption benchmark value, wherein the theoretical energy consumption is the ratio of the personalized energy consumption benchmark value to the standard test mileage.

[0191] Step 501: Generate the initial theoretical driving range based on the ratio of the user's current battery capacity to the theoretical energy consumption per kilometer.

[0192] Step 502: Real-time acquisition of slope change, speed fluctuation, and start-stop frequency in the current driving scenario; real-time correction of the initial theoretical driving range using preset dynamic adjustment rules; and generation of a personalized driving range recommendation value for the user's current scenario, specifically including:

[0193] Step 5020: Based on the gradient change range in the current driving scenario, determine the gradient reduction coefficient corresponding to the gradient change range using a preset gradient reduction mapping table; the gradient reduction mapping table defines the correspondence between different gradient ranges and reduction ratios. Based on the driving speed fluctuations in the current driving scenario, determine the speed compensation coefficient corresponding to the driving speed fluctuations using a preset speed compensation mapping table; the speed compensation mapping table defines the correspondence between the standard deviation range of speed fluctuations and the compensation ratio. Based on the start-stop frequency in the current driving scenario, determine the start-stop loss coefficient corresponding to the start-stop frequency using a preset start-stop loss mapping table; the start-stop loss mapping table defines the correspondence between the range of start-stop frequency per unit time and the loss ratio.

[0194] Step 5021: The gradient reduction factor, speed compensation factor, and start-stop loss factor are superimposed on the initial theoretical range to generate a personalized range recommendation value for the user's current scenario.

[0195] Step 503: The personalized recommended driving range value is updated and fed back to the user in real time through the display module of the vehicle terminal.

[0196] In this embodiment of the invention, after obtaining the user's personalized energy consumption baseline value, the theoretical energy consumption per kilometer is further calculated. The personalized energy consumption baseline value is the average energy consumption data of the vehicle under a specific usage scenario, obtained by comprehensively considering factors such as vehicle dynamic stability, driving behavior, and gradient.

[0197] The standard test mileage is a fixed reference value, set at 1 kilometer, used to standardize energy consumption. The theoretical energy consumption per kilometer is calculated by comparing the personalized energy consumption baseline value with the standard test mileage. For example, if a user's personalized energy consumption baseline value is 30Wh and the standard test mileage is 1 kilometer, then the theoretical energy consumption per kilometer = 30Wh ÷ 1 kilometer = 30Wh / km. This value represents the estimated electricity consumption per kilometer the vehicle travels under the current user's driving scenario and vehicle status.

[0198] Step 501: Obtain the current battery capacity data in real time. This data is provided by the vehicle's battery management system and reflects the current remaining available power of the battery, also in watt-hours (Wh). Then, divide the current battery capacity by the theoretical energy consumption per kilometer to obtain the initial theoretical driving range.

[0199] For example, if the current battery capacity is 300Wh and the theoretical energy consumption is 30Wh / km, then the initial theoretical driving range = 300Wh ÷ 30Wh / km = 10km. This 10km is the theoretical distance the vehicle can still travel based on the current energy consumption level and the remaining battery charge, without considering other real-time changing factors.

[0200] Step 5020, Determine the slope reduction factor:

[0201] The system uses onboard sensors to collect real-time data on slope changes in the current driving scenario, for example, if the current road segment has an 8-degree slope. A pre-set slope reduction mapping table divides the slope range and assigns a corresponding reduction percentage to each range. For example, a reduction percentage of 5% corresponds to a slope of 0-3 degrees, 10% to 3-6 degrees, 15% to 6-9 degrees, and 20% to over 9 degrees. The collected slope data is compared with the range in the mapping table. When an 8-degree slope is detected, it is determined to belong to the 6-9 degree range, and the corresponding reduction percentage of 15% is extracted as the slope reduction coefficient. This means that when driving on a road segment with an 8-degree slope, the vehicle's range will be reduced by 15% due to the slope factor.

[0202] Speed ​​compensation coefficient determined:

[0203] The system continuously monitors vehicle speed fluctuations and measures the degree of speed fluctuation by calculating the speed standard deviation within a certain time window (e.g., 1 minute). Assume the calculated speed standard deviation is 8 km / h. The speed compensation mapping table defines the correspondence between different speed fluctuation standard deviation ranges and compensation ratios. For example, a standard deviation of 0-3 km / h corresponds to a compensation ratio of 3% (indicating stable speed and a potential 3% increase in range), 3-6 km / h corresponds to 0%, 6-9 km / h corresponds to -5% (indicating significant speed fluctuations and a 5% decrease in range), and above 9 km / h corresponds to -10%. The system compares the calculated speed standard deviation of 8 km / h with the mapping table, determines it falls within the 6-9 km / h range, and extracts -5% as the speed compensation coefficient.

[0204] Determination of start-stop loss coefficient:

[0205] The system tracks the number of start-stop cycles per unit time (e.g., 10 minutes) in real time. Assuming the vehicle started and stopped 12 times in the last 10 minutes, this equates to 1.2 start-stop cycles per minute. A start-stop loss mapping table defines the correspondence between the range of start-stop cycles per unit time and the loss ratio. For example, 0-0.5 start-stop cycles per minute corresponds to a loss ratio of 3%, 0.5-1 corresponds to 6%, 1-1.5 corresponds to 10%, and more than 1.5 corresponds to 15%. The system compares the statistically calculated start-stop frequency of 1.2 times / minute with the mapping table, determines it falls within the 1-1.5 cycle range, and extracts 10% as the start-stop loss coefficient.

[0206] Step 5021: Combine the gradient reduction coefficient, speed compensation coefficient, and start-stop loss coefficient determined in step 5020 with the initial theoretical driving range obtained in step 501 for comprehensive calculation. Specifically, the initial theoretical driving range is multiplied by each coefficient (note the sign of the coefficient; a positive coefficient increases the driving range, and a negative coefficient decreases it), and then these results are added to the initial theoretical driving range for calculation.

[0207] For example, if the initial theoretical driving range is 10 kilometers, the gradient reduction factor is -15%, the speed compensation factor is -5%, and the start-stop loss factor is -10%, then the calculation process is as follows:

[0208] The distance reduced due to the gradient = 10 km × 15% = 1.5 km;

[0209] The distance lost due to speed fluctuations = 10 km × 5% = 0.5 km;

[0210] The mileage reduced due to start-stop losses = 10 km × 10% = 1 km;

[0211] Personalized recommended driving range = 10 kilometers 1.5 km 0.5 km 1 kilometer = 7 kilometers.

[0212] Step 503: The calculated personalized driving range recommendation is sent to the display module of the in-vehicle terminal. The in-vehicle terminal is usually the vehicle's instrument panel or central control display screen. The display module updates and displays the recommendation value in real time in a clear and intuitive way (such as numbers, progress bars, etc.). In this way, users can check the remaining driving range of the vehicle in the current scenario at any time while driving, making it easier to plan their trip and arrange charging time reasonably.

[0213] This method improves the accuracy and practicality of range prediction. Traditional range estimation often ignores complex factors in actual driving, leading to inaccurate range judgments and range anxiety for users. This method, however, comprehensively considers personalized energy consumption benchmarks as well as real-time factors such as gradient, speed fluctuations, and start-stop frequency, providing users with range data that closely reflects actual driving conditions. This helps users plan their routes and charging schedules more rationally, reducing uncertainties during their journeys.

[0214] In terms of vehicle intelligence, the real-time feedback mechanism enables the vehicle to dynamically adjust the range display based on changes in the environment and driving behavior, enhancing the vehicle's intelligent interactivity. Users can intuitively perceive the impact of different driving styles and road conditions on vehicle energy consumption through changes in the recommended range, thereby consciously adjusting their driving habits to achieve energy-saving driving.

[0215] like Figure 2 As shown, embodiments of the present invention also provide a personalized range recommendation system for electric motorcycle users, including:

[0216] The basic parameter acquisition module is used to acquire the battery capacity, motor power, and average energy consumption data of the electric motorcycle under standard test conditions.

[0217] The driving data acquisition module is used to collect real-time driving data based on basic parameters, such as the user's driving road conditions, slope changes, driving speed fluctuations, start-stop frequency, acceleration or braking behavior, as well as to detect the real-time coordinates of the battery pack center position, motor installation position and vehicle center of gravity position of the electric motorcycle through on-board sensors.

[0218] The dynamic relationship analysis module is used to analyze the dynamic spatial relationship changes of the battery pack center position, motor mounting position and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in user driving behavior and the slope change amplitude in environmental energy consumption influencing factors, dynamic energy consumption correction coefficients are generated.

[0219] The energy consumption benchmark generation module is used to integrate average energy consumption data with dynamic energy consumption correction coefficients to obtain personalized energy consumption benchmark values ​​for users.

[0220] The driving range recommendation module is used to dynamically generate a personalized driving range recommendation value for the user's current usage scenario based on the personalized energy consumption benchmark value and battery capacity, and then feed it back to the user in real time through the vehicle terminal.

[0221] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0222] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0223] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0224] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for recommending personalized range for a user of an electric motorcycle, characterized in that, The method includes: Step 1: Obtain the basic parameters of the electric motorcycle, including battery capacity, motor power, and average energy consumption data under standard test conditions; Step 2: Based on the basic parameters, collect the user's actual driving data in real time. The actual driving data includes road condition type, slope change, driving speed fluctuation, start-stop frequency, acceleration or braking behavior, and real-time coordinates of the battery pack center position, motor installation position and vehicle center of gravity position of the electric motorcycle detected by on-board sensors. Step 3: Analyze the dynamic spatial relationship characteristics of the battery pack center position, motor mounting position, and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in user driving behavior and the slope variation amplitude among environmental energy consumption influencing factors, generate dynamic energy consumption correction coefficients. This includes: calculating the first real-time displacement vector between the battery pack center position and the motor mounting position, the second real-time displacement vector between the motor mounting position and the vehicle center of gravity position, and the third real-time displacement vector between the vehicle center of gravity position and the battery pack center position, based on the real-time coordinates of the battery pack center position, motor mounting position, and vehicle center of gravity position; and calculating the third real-time displacement vector between the battery pack center position and the vehicle center of gravity position based on the first real-time displacement vector. The real-time change trends of the second and third real-time displacement vectors are used to calculate the displacement fluctuation amplitude of the battery pack center position, motor mounting position, and vehicle center of gravity position within a preset time window, generating distribution stability parameters. Based on the distribution stability parameters, combined with the acceleration or braking frequency in user driving behavior, the first influence weight of driving behavior on energy consumption is quantified, and combined with the slope change amplitude among environmental energy consumption influencing factors, the second influence weight of slope on energy consumption is quantified, resulting in quantified driving behavior influence weights and slope additional weights. The displacement fluctuation amplitude, distribution stability parameters, quantified driving behavior influence weights, and slope additional weights are then integrated to generate a dynamic energy consumption correction coefficient. Step 4: Combine the average energy consumption data with the dynamic energy consumption correction coefficient to obtain the user's personalized energy consumption benchmark value. Step 5: Based on the personalized energy consumption baseline value and battery capacity, dynamically generate a personalized range recommendation value for the user's current usage scenario, and feed it back to the user in real time through the vehicle terminal.

2. The method for recommending personalized driving range for electric motorcycle users according to claim 1, characterized in that, Based on the real-time change trends of the first, second, and third real-time displacement vectors, the displacement fluctuation amplitudes of the battery pack center position, motor mounting position, and vehicle center of gravity position within a preset time window are calculated, and distribution stability parameters are generated, including: Based on the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, the displacement of each displacement vector at each sampling point within a preset time window is extracted, and the maximum displacement, minimum displacement, and average displacement within the time window are calculated. For each displacement vector, the displacement range within the time window is calculated based on the absolute difference between the maximum and minimum displacements. The absolute deviation between the displacement at each sampling point and the benchmark value is calculated using the average displacement as the benchmark value. The deviation magnitude is obtained by averaging the absolute deviations of all sampling points within the time window. The displacement range and deviation amplitude are weighted and summed according to a preset ratio to generate the comprehensive displacement fluctuation amplitude of each displacement vector. The combined displacement fluctuation amplitude of the three displacement vectors is normalized to generate distributed stability parameters that characterize the dynamic stability of the vehicle.

3. The method for recommending personalized range for electric motorcycle users according to claim 2, characterized in that, By fusing average energy consumption data with dynamic energy consumption correction factors, a user-personalized energy consumption benchmark value is obtained, including: Based on the distributed stability parameters, the first correction ratio of vehicle dynamic stability to energy consumption is determined through a preset stability correction mapping table. Based on the first correction ratio, and combined with the influence weight of driving behavior and the additional weight of slope, the second correction ratio of driving behavior on energy consumption and the third correction ratio of slope on energy consumption are quantified through the driving behavior correction mapping table and the slope correction mapping table, respectively. The first, second, and third correction ratios are superimposed on the average energy consumption data under the standard test environment to generate a user-personalized energy consumption benchmark value.

4. The method for recommending personalized range for electric motorcycle users according to claim 3, characterized in that, Based on the first correction ratio, and combined with the weighting of driving behavior and the additional weighting of slope, the second correction ratio of driving behavior on energy consumption and the third correction ratio of slope on energy consumption are quantified using driving behavior correction mapping tables and slope correction mapping tables, respectively, including: Based on the acceleration or braking frequency in the user's driving behavior, the correction interval to which the acceleration or braking frequency belongs is determined through a preset driving behavior correction mapping table, and the corresponding second correction ratio is extracted; the driving behavior correction mapping table defines a linear correspondence between different acceleration or braking frequency ranges and correction ratios. Based on the slope variation range among the environmental energy consumption influencing factors, a preset slope correction mapping table is used to determine the correction interval to which the slope variation range belongs, and the corresponding third correction ratio is extracted; the slope correction mapping table defines a nonlinear correspondence between different slope variation ranges and correction ratios.

5. The method for recommending personalized range for electric motorcycle users according to claim 4, characterized in that, Based on personalized energy consumption baselines and battery capacity, a personalized recommended driving range for the user's current usage scenario is dynamically generated and fed back to the user in real time via the in-vehicle terminal, including: Based on the user's personalized energy consumption benchmark, the theoretical energy consumption per kilometer is calculated, where the theoretical energy consumption is the ratio of the personalized energy consumption benchmark to the standard test mileage. The initial theoretical driving range is generated based on the ratio of the user's current battery capacity to the theoretical energy consumption per kilometer. The system can acquire the slope change range, speed fluctuation and start-stop frequency in the current driving scenario in real time, and make real-time corrections to the initial theoretical driving range through preset dynamic adjustment rules to generate a personalized driving range recommendation value for the user in the current scenario. The vehicle's onboard terminal displays and provides users with personalized recommended driving range values ​​in real time.

6. The method for recommending personalized range for electric motorcycle users according to claim 5, characterized in that, It acquires real-time data on gradient changes, speed fluctuations, and start-stop frequency in the current driving scenario, and uses preset dynamic adjustment rules to correct the initial theoretical driving range in real time, generating a personalized driving range recommendation value for the user's current scenario, including: Based on the gradient change range in the current driving scenario, a gradient reduction coefficient corresponding to the gradient change range is determined through a preset gradient reduction mapping table; the gradient reduction mapping table defines the correspondence between different gradient ranges and reduction ratios. Based on the driving speed fluctuation in the current driving scenario, a speed compensation coefficient corresponding to the driving speed fluctuation is determined through a preset speed compensation mapping table; the speed compensation mapping table defines the correspondence between the standard deviation range of speed fluctuations and the compensation ratio. Based on the start-stop frequency in the current driving scenario, a start-stop loss coefficient corresponding to the start-stop frequency is determined through a preset start-stop loss mapping table; the start-stop loss mapping table defines the correspondence between the range of start-stop frequency per unit time and the loss ratio. The gradient reduction factor, speed compensation factor, and start-stop loss factor are superimposed on the initial theoretical range to generate a personalized range recommendation value for the user's current scenario.

7. A personalized range recommendation system for electric motorcycle users, wherein the system implements the method as described in any one of claims 1 to 6, characterized in that, include: The basic parameter acquisition module is used to acquire the battery capacity, motor power, and average energy consumption data of the electric motorcycle under standard test conditions. The driving data acquisition module is used to collect real-time driving data based on basic parameters, such as the user's driving road conditions, slope changes, driving speed fluctuations, start-stop frequency, acceleration or braking behavior, as well as to detect the real-time coordinates of the battery pack center position, motor installation position and vehicle center of gravity position of the electric motorcycle through on-board sensors. The dynamic relationship analysis module is used to analyze the dynamic spatial relationship changes of the battery pack center position, motor mounting position and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in user driving behavior and the slope change amplitude in environmental energy consumption influencing factors, dynamic energy consumption correction coefficients are generated. The energy consumption benchmark generation module is used to integrate average energy consumption data with dynamic energy consumption correction coefficients to obtain personalized energy consumption benchmark values ​​for users. The driving range recommendation module is used to dynamically generate a personalized driving range recommendation value for the user's current usage scenario based on the personalized energy consumption benchmark value and battery capacity, and then feed it back to the user in real time through the vehicle terminal.

8. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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