Personalized endurance mileage recommendation method and system for battery motorcycle user

Abstract

The invention provides a personalized endurance mileage recommendation method and system for a storage battery motorcycle user, and relates to the technical field of data processing, and the method comprises the steps: 1, obtaining basic parameters of a storage battery motorcycle, including battery capacity, motor power and average energy consumption data in a standard test environment; 2, on the basis of the basic parameters, actual driving data of the user are collected in real time, and the actual driving data comprise driving road condition types, gradient changes, driving speed fluctuations, starting and stopping frequencies and acceleration or braking behaviors; and real-time coordinates of the battery pack center position, the motor installation position and the vehicle gravity center position of the storage battery motorcycle are detected in real time through a vehicle-mounted sensor. According to the method, the personalized endurance mileage recommendation value is accurately generated and fed back in real time by fusing the vehicle basic parameters, the real-time driving data and the dynamic spatial relationship analysis, so that the mileage anxiety of the user is effectively relieved, and the driving experience and the vehicle energy efficiency optimization capability are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for recommending personalized cruising range to battery motorcycle users. Background Art

[0002] Currently, range estimates for electric motorcycles are generally based on average energy consumption data under standard testing conditions, which fails to accurately reflect the range in actual user scenarios. For example, user A frequently starts and stops and accelerates to overtake during their daily commute, while user B has a more stable driving style. However, traditional estimation methods use the same standard data to calculate the range of both, completely ignoring these differences in driving behavior. Furthermore, traditional methods overlook factors such as vehicle dynamics and complex road conditions, resulting in significant deviations between range predictions and actual range, exacerbating users' "range anxiety."

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

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

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] In a first aspect, a method for recommending personalized cruising range for battery motorcycle users is provided, the method comprising:

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

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

[0009] Step 3: Analyze the changing characteristics of the dynamic spatial relationship between the battery pack center position, motor installation position, and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in the user's driving behavior and the slope change amplitude in the environmental energy consumption influencing factors, a dynamic energy consumption correction coefficient is generated.

[0010] Step 4: The average energy consumption data is integrated 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 benchmark value and battery capacity, dynamically generate a personalized range recommendation value for the user's current usage scenario, and provide real-time feedback to the user through the in-vehicle terminal.

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

[0013] Based on the real-time coordinates of the battery pack center position, the motor installation position, and the vehicle center of gravity, respectively calculate a first real-time displacement vector between the battery pack center position and the motor installation position, a second real-time displacement vector between the motor installation position and the vehicle center of gravity position, and a third real-time displacement vector between the vehicle center of gravity position and the battery pack center position;

[0014] Calculate the displacement fluctuation amplitudes of the battery pack center position, the motor installation position, and the vehicle center of gravity within a preset time window based on the real-time change trends of the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, and generate a distribution stability parameter;

[0015] Based on the distribution stability parameter and the acceleration or braking frequency in the user's driving behavior, the first impact weight of driving behavior on energy consumption is quantified. In addition, the second impact weight of slope on energy consumption is quantified based on the slope change amplitude in the environmental energy consumption influencing factors. The quantified driving behavior impact weight and slope additional weight are obtained.

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

[0017] Furthermore, based on the real-time change trends of the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, the displacement fluctuation amplitudes of the battery pack center position, the motor installation position, and the vehicle center of gravity position within a preset time window are calculated to generate 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, respectively extracting the displacement of each displacement vector at each sampling point within a preset time window, and counting the maximum displacement, minimum displacement, and average displacement within the time window;

[0019] For each displacement vector, the displacement range within the time window is calculated based on the absolute difference between the maximum displacement and the minimum displacement. The absolute deviation between the displacement of each sampling point and the reference value is calculated using the average displacement as the reference value. The absolute deviation of all sampling points in the time window is averaged to obtain the deviation amplitude.

[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 comprehensive displacement fluctuation amplitudes of the three displacement vectors are normalized to generate the 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 coefficient to obtain the user's personalized energy consumption benchmark value, including:

[0023] Based on the distribution stability parameter, determining a first correction ratio of the vehicle dynamic stability to energy consumption through a preset stability correction mapping table;

[0024] According to the first correction ratio, combined with the driving behavior influence weight and the slope additional weight, the second correction ratio of driving behavior to energy consumption and the third correction ratio of slope to energy consumption are quantified using the driving behavior correction mapping table and the slope correction mapping table respectively;

[0025] 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.

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

[0027] Based on the acceleration or braking frequency in the user's driving behavior, a preset driving behavior correction mapping table is used to determine the correction interval to which the acceleration or braking frequency belongs, and a 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 change amplitude in the environmental energy consumption influencing factors, the correction interval to which the slope change amplitude belongs is determined through a preset slope correction mapping table, and the corresponding third correction ratio is extracted; the slope correction mapping table defines a nonlinear correspondence between different slope change amplitude ranges and correction ratios.

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

[0030] Calculate the theoretical energy consumption per kilometer based on the user's personalized energy consumption benchmark value, where the theoretical energy consumption is the ratio of the personalized energy consumption benchmark value to the standard test mileage;

[0031] Generate the initial theoretical range based on the ratio of the user's current battery capacity to the theoretical energy consumption per kilometer;

[0032] Real-time acquisition of slope changes, driving speed fluctuations, and start-stop frequency in the current driving scenario. Using preset dynamic adjustment rules, the initial theoretical range is corrected in real time to generate a personalized range recommendation for the user's current scenario.

[0033] The personalized range recommendation value is updated in real time and fed back to the user through the display module of the vehicle terminal.

[0034] Furthermore, the system obtains the slope change amplitude, driving speed fluctuation, and start-stop frequency in the current driving scenario in real time, and uses preset dynamic adjustment rules to make real-time corrections to the initial theoretical cruising range to generate a personalized cruising range recommendation for the user's current scenario, including:

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

[0036] The slope reduction coefficient, speed compensation coefficient and start-stop loss coefficient are added to the initial theoretical cruising range to generate a personalized cruising range recommendation value for the user's current scenario.

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

[0038] Basic parameter acquisition module, used to obtain the battery capacity, motor power and average energy consumption data of the battery motorcycle under standard test conditions;

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

[0040] The dynamic relationship analysis module analyzes the changing characteristics of the dynamic spatial relationship between the battery pack center position, motor installation position, and vehicle center of gravity, including relative displacement and distribution stability parameters. It also generates a dynamic energy consumption correction factor based on the acceleration or braking frequency in the user's driving behavior and the slope variation amplitude in the environmental energy consumption influencing factors.

[0041] Energy consumption benchmark generation module, used to integrate average energy consumption data with dynamic energy consumption correction coefficient to obtain user-personalized energy consumption benchmark value;

[0042] The cruising range recommendation module is used to dynamically generate personalized cruising range recommendation values ​​for the user's current usage scenario based on personalized energy consumption benchmark values ​​and battery capacity, and provide real-time feedback to the user through the in-vehicle terminal.

[0043] According to a third aspect, a computing device includes:

[0044] one or more processors;

[0045] The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0046] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

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

[0048] By integrating basic vehicle parameters and real-time driving data, covering various factors such as driving behavior, road conditions, slope, etc., the dynamic spatial relationship between the battery pack, motor and vehicle center of gravity is analyzed, a dynamic energy consumption correction coefficient is constructed, and personalized energy consumption benchmark values ​​are accurately calculated, making the recommended cruising range value closer to the actual situation, effectively alleviating users' concerns about power consumption and facilitating reasonable itinerary planning.

[0049] By collecting data in real time from onboard sensors, the system dynamically generates a range recommendation and promptly sends feedback to the vehicle terminal. This allows users to obtain accurate range information at any time while driving, enhancing their sense of control over the vehicle's status. Furthermore, this real-time feedback mechanism can encourage users to adjust their driving habits, such as adopting a more energy-efficient driving style when range is low, indirectly improving energy efficiency.

[0050] The vast amount of multi-dimensional data collected provides a rich resource for optimizing battery motorcycle technology. Manufacturers can analyze user-specific energy consumption data and vehicle dynamic characteristics to improve battery management systems, optimize motor design, and enhance 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention provides a flowchart of a method for recommending personalized cruising range for battery motorcycle users.

[0052] Figure 2 Schematic diagram of a personalized range recommendation system for battery motorcycle users provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0054] like Figure 1 As shown, an embodiment of the present invention provides a method for recommending personalized cruising range for battery motorcycle users, the method comprising the following steps:

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

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

[0057] Step 3: Analyze the changing characteristics of the dynamic spatial relationship between the battery pack center position, motor installation position, and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in the user's driving behavior and the slope change amplitude in the environmental energy consumption influencing factors, a dynamic energy consumption correction coefficient is generated.

[0058] Step 4: The average energy consumption data is integrated 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 benchmark value and battery capacity, dynamically generate a personalized range recommendation value for the user's current usage scenario, and provide real-time feedback to the user through the in-vehicle terminal.

[0060] In this embodiment of the present invention, by collecting real-time user driving behavior data (such as acceleration and braking frequency, start-stop patterns), combined with dynamic spatial relationship analysis of the vehicle, accurate modeling of different user driving habits is achieved. This shifts range prediction from a "standardized" approach to a "customer-specific" approach, improving the alignment of prediction results with actual usage. Onboard sensors monitor the dynamic changes of the battery pack, motor, and center of gravity in real time, capturing subtle energy consumption characteristics during vehicle operation (such as component vibration and center of gravity shift). Correction factors are adjusted in real time based on environmental factors such as slope and road conditions, ensuring that range predictions consistently reflect the current real-world scenario.

[0061] By combining vehicle structural dynamics parameters (relative displacement of components and distribution stability) with traditional energy consumption data, this approach transcends the limitations of existing technologies that rely solely on single-dimensional analysis of driving behavior or environmental factors, forming a more comprehensive and in-depth system of factors influencing energy consumption. Range recommendations are updated instantly via the on-board terminal, allowing users to intuitively access range information tailored to their current driving state, effectively alleviating range anxiety. Simultaneously, by continuously learning from user behavior patterns, prediction accuracy is gradually improved. Specific optimizations are implemented for complex road conditions (such as frequent start-stop urban roads and mountainous sections with continuous hills) and specific driving styles (aggressive vs. steady), maintaining high prediction accuracy even in extreme scenarios. By analyzing the correlation between dynamic spatial relationships and energy consumption, this system indirectly provides data support for vehicle design optimization (such as evaluating component layout rationality). Furthermore, users can proactively adjust their driving habits to improve energy efficiency based on feedback on range trends, forming a closed loop of "prediction-feedback-optimization."

[0062] In a preferred embodiment of the present invention, the above step 1 obtains basic parameters of the battery motorcycle, including battery capacity, motor power, and average energy consumption data under a standard test environment; the above step 2 collects the user's actual driving data in real time based on the basic parameters, wherein the actual driving data includes the type of driving road condition, slope change, driving speed fluctuation, start-stop frequency, acceleration or braking behavior, and the real-time coordinates of the battery motorcycle's battery pack center position, motor installation position, and vehicle center of gravity position detected in real time by on-board sensors, and may include:

[0063] In an embodiment of the present invention, when a vehicle rolls off the production line, the battery manufacturer writes the nominal capacity (e.g., 100Ah) to the on-board ECU (electronic control unit) through 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), which calculates the remaining capacity through the coulomb counting method (accumulated charge and discharge current) combined with ampere-hour integration and compensates for self-discharge losses.

[0066] Aging correction: The ECU maintains a battery state of health (SOH) curve and automatically degrades the nominal capacity based on the number of charge and discharge cycles (collected via the CAN bus) and temperature history. For example, if the SOH drops to 85% after 500 cycles, the actual usable capacity is updated to 85Ah.

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

[0068] Instantaneous power = bus voltage × bus current × controller efficiency (95% by default). 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 a hilly road (slope > 5°), the ECU automatically retrieves the motor overload characteristic curve, allows for short-term peak power output, and records the overload duration to assess the impact of motor temperature rise.

[0069] Laboratory testing process:

[0070] Test equipment: Chassis dynamometer simulates different road resistances, environmental chamber controls temperature (25°C ± 2°C) and humidity (60% ± 5%).

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

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

[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 call of benchmark data according to the current driving scenario.

[0074] Main sensor: A high-precision GNSS module (such as the u-blox ZED-F9P) provides centimeter-level positioning accuracy and a built-in electronic compass to distinguish the direction of travel.

[0075] Auxiliary sensor: A front-facing camera (5 megapixels, 30 fps) equipped with a MobileNetV3 model for real-time recognition of traffic signs (such as speed limits and congestion warnings).

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

[0077] Visual verification: When the camera detects that the distance between the vehicle ahead is less than 5 meters and lasts for more than 1 minute, the "congestion" mark is triggered, overwriting the map classification result.

[0078] Data output: The traffic condition label (e.g., “city congestion, low speed, frequent starts and stops”) is updated every second and stored in a circular buffer.

[0079] Attitude solution process:

[0080] Accelerometer: Collects X (longitudinal), Y (lateral), and Z (vertical) accelerations, removes vibration noise through low-pass filtering (cutoff frequency 10 Hz), and extracts the vertical acceleration component (Z axis) to calculate the slope sine value;

[0081] Gyroscope: Measures pitch rate, integrates it to obtain the real-time pitch angle, and uses it to correct the slope calculation based on the vehicle's wheelbase (e.g., for a wheelbase of 1.5 meters, a pitch angle of 1° corresponds to a slope of ≈1.745%).

[0082] GNSS altitude data is synchronized every minute (accuracy: ±1 meter). The ratio of the altitude difference between adjacent moments to the distance traveled is calculated to correct for sensor slope errors. For example, if the altitude rises 5 meters after driving 100 meters, the slope is determined to be 5%. This is then weighted averaged with the sensor-calculated value (70% sensor weight, 30% altitude weight).

[0083] The speed sensor uses a magnetoelectric encoder (installed on the wheel 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 1 second to calculate the average speed and speed standard deviation. The speed standard deviation reflects the severity of fluctuations. A speed standard deviation greater than 5 km / h is considered "severe fluctuations." The number of sudden accelerations is the number of events in which the speed jumps greater than 10 km / h and lasts less than 2 seconds. When the speed standard deviation is greater than 8 km / h and the number of sudden accelerations is greater than 3 times / minute, it is marked as an "aggressive driving - high-speed fluctuation" scenario.

[0086] Start-stop frequency:

[0087] Effective parking: The speed is ≤ 0 km / h and lasts for ≥ 3 seconds (excluding temporary coasting), triggering a parking event;

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

[0089] In the 10-minute statistical period:

[0090] Start-stop frequency ≤ 3 times: mark "smooth driving";

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

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

[0093] Acceleration or braking behavior:

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

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

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

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

[0098] Each time an acceleration or braking event is triggered, the following are recorded: event type (light / urgent); duration (e.g., rapid acceleration lasts 2.5 seconds); and energy change, which is calculated by integrating the motor power curve to calculate the additional energy consumption (e.g., rapid acceleration consumes 50Wh).

[0099] Vehicle component location coordinates:

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

[0101] Motor installation position: Fix the laser displacement sensor 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: The real-time center of gravity coordinates (the origin of the coordinate system is set to the center of the front axle) are calculated using an onboard IMU (such as the MPU9250) combined with a vehicle mass distribution model (presumably a 40kg battery, 15kg motor, and 25kg frame).

[0103] The vehicle automatically enters "calibration mode" at the first start of each week:

[0104] Remain 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 sensor offsets (such as displacement errors caused by temperature drift).

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

[0106] Preprocessing pipeline:

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

[0108] Leakage filling: If sensor data is lost (e.g., GNSS signal obstruction), the previous value hold method (HoldLastValue) is used to fill in the gaps until the signal is restored.

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

[0110] In a preferred embodiment of the present invention, step 3 above analyzes the changing characteristics of the dynamic spatial relationship between the battery pack center position, the motor installation position, and the vehicle center of gravity position, including the relative displacement and distribution stability parameters, and generates a dynamic energy consumption correction coefficient based on the acceleration or braking frequency in the user's driving behavior and the slope change amplitude in the environmental energy consumption influencing factors. The dynamic energy consumption correction coefficient may include:

[0111] Step 300, based on the real-time coordinates of the battery pack center position, the motor installation position, and the vehicle center of gravity, respectively calculate a first real-time displacement vector between the battery pack center position and the motor installation position, a second real-time displacement vector between the motor installation position and the vehicle center of gravity position, and a third real-time displacement vector between the vehicle center of gravity position and the battery pack center position;

[0112] Step 301 calculates the displacement fluctuation amplitudes of the battery pack center position, the motor installation position, and the vehicle center of gravity position within a preset time window based on the real-time change trends of the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, and generates a distribution stability parameter, 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, respectively extract the displacement of each displacement vector at each sampling point within a preset time window, 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 displacement and the minimum displacement. Using the average displacement as a reference value, calculate the absolute deviation between the displacement of each sampling point and the reference value. Average the absolute deviations of all sampling points within the time window to obtain the deviation amplitude.

[0115] Step 3012: Perform weighted summation of the displacement range and the deviation amplitude according to a preset ratio to generate a comprehensive displacement fluctuation amplitude for each displacement vector;

[0116] Step 3013: normalize the comprehensive displacement fluctuation amplitudes of the three displacement vectors to generate a distributed stability parameter representing the dynamic stability of the vehicle;

[0117] Step 302: Based on the distribution stability parameter and the acceleration or braking frequency in the user's driving behavior, a first impact weight of the driving behavior on energy consumption is quantified. Furthermore, based on the slope variation amplitude in the environmental energy consumption influencing factor, a second impact weight of the slope on energy consumption is quantified to obtain a quantified driving behavior impact weight and a slope additional weight.

[0118] In step 303 , the displacement fluctuation amplitude, the distribution stability parameter, the quantified driving behavior influence weight, and the slope additional weight are integrated to generate a dynamic energy consumption correction coefficient.

[0119] In an embodiment of the present invention, when the battery motorcycle is running, high-precision sensors (such as laser displacement sensors and inertial measurement units) installed at the center of the battery pack, the motor installation position and the center of gravity of the vehicle will continuously collect data. These sensors are like the "electronic eyes" of the vehicle, monitoring the position of components at all times.

[0120] Taking the vehicle frame as the reference coordinate system is like setting up a "fixed room" for the vehicle. The positions of all components are determined relative to this "room", and the coordinate data collected by the sensor is uniformly converted to this vehicle frame coordinate system.

[0121] When calculating the specific position, for example, the first real-time displacement vector between the center position of the battery pack and the motor installation position will be calculated using the real-time coordinates of the center position of the battery pack in the frame coordinate system. , minus the real-time coordinates of the motor installation position in the same coordinate system The obtained three-dimensional coordinate difference ( ) is the first real-time displacement vector. This vector indicates how much the center of the battery pack has moved relative to the motor's mounting position in the forward, backward, left, right, and up and down directions. Similarly, through similar subtraction operations, we obtain 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). These displacement vectors change in real time as the vehicle bumps, turns, accelerates, and decelerates.

[0122] Step 3010, first set a preset time window, assuming it is 1 minute, and observe the changes in the displacement vector during this period. In this 1 minute, the sensor collects data once a second, so there will be 60 sampling points. For each real-time displacement vector, calculate its displacement at each sampling point. The displacement here is the length of the displacement vector. You can imagine it as a directional rope, and you want to measure the length of this rope. The specific calculation method is to use the Pythagorean theorem to square and square the components of the displacement vector on the three coordinate axes (that is, the coordinate difference). For example, if the components of a displacement vector on the three coordinate axes are 3 cm, 4 cm, and 0 cm respectively, then its displacement is centimeter.

[0123] During the statistical process, we carefully record the maximum, minimum, and average displacement of each displacement vector within that one-minute period. The maximum displacement is the largest displacement value among the 60 sampling points, and the minimum displacement is the smallest value. The average displacement is simply the sum of the displacement values ​​of all 60 sampling points and divided by 60. These statistical values ​​clearly indicate the maximum and minimum ranges of displacement vector variation, as well as the overall average level of variation, within that one-minute period.

[0124] In step 3011, for each displacement vector, the displacement range is first calculated. The displacement range is the absolute difference between the maximum and minimum displacements. For example, if the maximum displacement of a displacement vector is 8 cm and the minimum displacement is 2 cm, the displacement range is |8 - 2| = 6 cm. This value intuitively reflects the fluctuation range of the displacement within a 1-minute time window. A larger value indicates a more drastic displacement change.

[0125] Next, take the average displacement as the reference value. For each sampling point, calculate the absolute deviation between its displacement and the average displacement. 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 3|=2 cm. Then, add up the absolute deviations for all 60 sampling points within the time window and divide by 60 to obtain the deviation amplitude. The deviation amplitude reflects the degree of dispersion of the displacement at each sampling point relative to the average level. A larger deviation amplitude indicates more unstable displacement fluctuations.

[0126] In step 3012, a preset ratio is set for the displacement range and the deviation amplitude. Assume that the displacement range accounts for 40% and the deviation amplitude accounts for 60%. For each displacement vector, its displacement range is multiplied by 40%, and its deviation amplitude is multiplied by 60%. These two results are then added together to obtain the comprehensive displacement fluctuation amplitude for that displacement vector. This calculated comprehensive displacement fluctuation amplitude takes into account both the displacement variation range (displacement range) and the degree of dispersion of the displacement relative to the average level (deviation amplitude), providing a more comprehensive and accurate reflection of the fluctuation characteristics of the displacement vector.

[0127] Step 3013, normalize the comprehensive displacement fluctuation amplitudes of the three displacement vectors. Normalization is like "converting" values ​​of different sizes to a unified range of 0 to 1. Specifically, divide each comprehensive displacement fluctuation amplitude by the maximum value of the three comprehensive displacement fluctuation amplitudes. For example, if the three comprehensive displacement fluctuation amplitudes are 10, 8, and 6, respectively, 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 the three normalized values. Assuming the same weights, that is, add the three normalized values ​​and divide them by 3. The result is the distributed stability parameter that characterizes the dynamic stability of the vehicle. The larger the value of this parameter, the more drastic the change in the relative positions of the battery pack, motor, and center of gravity during the vehicle's driving, and the worse the dynamic stability of the vehicle; conversely, the smaller the value, the better the dynamic stability of the vehicle.

[0128] In step 302, the analysis process of the first impact weight (driving behavior) is as follows:

[0129] Distribution stability parameter: reflects the severity of the relative position changes 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, it is determined that the driving behavior has a significant impact on energy consumption, and the first impact weight range is 60%-80%;

[0132] When the distribution stability parameter is medium (threshold -Threshold A) and the acceleration or braking frequency is moderate (Threshold -Threshold B), the first impact weight range is 30%-60%;

[0133] When both the parameter and the frequency are lower than the threshold of 50%, the impact is judged to be weak, and the first impact weight range is 10%-30%.

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

[0135] Driving factor: The magnitude of the slope change reflects the terrain's demand on vehicle power output. A critical slope value, Y (unit: degrees), is set as the evaluation benchmark. This value, determined based on extensive on-road testing, is used to categorize the impact of slope 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 impact weight range is 40%-60%. For example, on a continuous climbing section in a mountainous area, the vehicle needs to continuously output high power, and a high weight can effectively reflect the impact of slope on energy consumption.

[0137] When the slope changes in When the slope is between 0 and Y, the terrain influence is judged to be moderate, and the weight range is 20%-40%. This situation is common in undulating roads in suburban areas, where the slope has a certain impact on energy consumption but is not dominant.

[0138] When the slope change is less than When the slope is determined to have a small impact on energy consumption, the weight range is 10%-20%. If driving on a flat road in the city, 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 regression analysis algorithm was used to establish a mapping between parameter combinations and weight values. For example, if the distribution stability parameter is high, the acceleration or braking frequency is high, and the slope variation is low, the first weight is 70% and the second weight is 15%. For example, if the distribution stability parameter is medium, the acceleration or braking frequency is low, and the slope variation is medium, the first weight is 40% and the second weight is 35%.

[0140] Step 303, dynamic energy consumption correction coefficient = , where the factor values ​​include:

[0141] Displacement fluctuation amplitude (comprehensive value): reflects the displacement stability during vehicle operation, with a weight 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% in vibration-sensitive scenarios and 25% in stable scenarios).

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

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

[0145] Assume a scenario:

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

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

[0148] Driving behavior impact weight = 70%, corresponding weight 30%;

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

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

[0151] By calculating the dynamic energy consumption correction factor, we can comprehensively consider the impact of the dynamic spatial relationships of vehicle components, driving behavior, and environmental factors on energy consumption. Specifically, by accurately analyzing the relative displacement and distribution stability of components, we can capture changes in energy consumption caused by vibration, center of gravity shift, and other factors during vehicle operation. Quantifying the weight of driving behavior and slope influences allows for precise corrections based on different driving styles and road conditions. The resulting dynamic energy consumption correction factor improves the accuracy of range predictions, making them more relevant to users' actual usage scenarios, effectively alleviating range anxiety, and providing data support for optimizing vehicle energy consumption and improving performance.

[0152] In a preferred embodiment of the present invention, the above step 4, fusing the average energy consumption data with the dynamic energy consumption correction coefficient to obtain the user's personalized energy consumption baseline value, may include:

[0153] Step 400 , determining a first correction ratio of vehicle dynamic stability to energy consumption based on the distribution stability parameter and using a preset stability correction mapping table;

[0154] Step 401 quantifies the second correction ratio of driving behavior to energy consumption and the third correction ratio of slope to energy consumption based on the first correction ratio, combined with the driving behavior influence weight and the slope additional weight, using a driving behavior correction mapping table and a slope correction mapping table, respectively. Specifically, the steps include:

[0155] Step 4010: Based on the acceleration or braking frequency in the user's driving behavior, a preset driving behavior correction mapping table is used to determine the correction interval to which the acceleration or braking frequency belongs, and a 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;

[0156] Step 4011: Based on the slope change amplitude in the environmental energy consumption influencing factor, a preset slope correction mapping table is used to determine the correction interval to which the slope change amplitude belongs, and a corresponding third correction ratio is extracted; the slope correction mapping table defines a nonlinear correspondence between different slope change amplitude ranges and correction ratios;

[0157] Step 402 : Add the first correction ratio, the second correction ratio, and the third correction ratio to 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 present invention, while the battery motorcycle is operating, the system continuously monitors the relative position changes of the battery pack, motor, and vehicle center of gravity, and calculates a distributed stability parameter. This parameter ranges from 0 to 1. Values ​​closer to 0 indicate more stable relative component positions during driving; values ​​closer to 1 indicate more drastic relative component displacement and poorer vehicle dynamic stability.

[0159] The system has a pre-built stability correction mapping table, which is used to determine the correction ratio corresponding to different stability states. It divides the distribution stability parameter into multiple intervals, such as:

[0160] Range 1 (Extremely High Stability): When the distributed stability parameter is between 0 and 0.1, 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 vehicle's dynamic stability has little impact on energy consumption, increasing it by only 3% over the standard value.

[0161] Range 2 (High Stability): If the distribution stability parameter is between 0.1 and 0.3, the vehicle has good stability and the relative displacement of components is slight. The first correction ratio corresponding to this range in the mapping table is 8%, which means that the vehicle's dynamic stability will increase energy consumption by 8%.

[0162] Interval 3 (Medium Stability): When the distribution stability parameter is in the range of 0.3-0.6, the relative displacement of the vehicle components during driving is more obvious, and the stability is at a medium level. At this time, the mapping table stipulates that the first correction ratio is 15%;

[0163] Interval 4 (low stability): If the distribution stability parameter is between 0.6 and 0.8, the displacement of vehicle components varies greatly, the stability is poor, and 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 components change dramatically while driving, and the vehicle is in an unstable state. The first correction ratio is set to 30%.

[0165] The current distribution stability parameter is read in real time, the interval it belongs to is determined, and the corresponding first correction ratio is found in the mapping table. For example, if the current calculated distribution stability parameter is 0.5, it is determined to be in "Interval 3 (Medium Stability)", and 15% is selected as the first correction ratio.

[0166] Step 4010 continuously records the user's acceleration or braking frequency during driving, 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 pre-set driving behavior correction mapping table. This table details 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 (Super 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 few acceleration or deceleration operations. In this case, the driving behavior correction map specifies a corresponding second correction ratio of 10%. This means that a smooth driving style has little impact on the increase in energy consumption, only increasing energy consumption by 10% based on the standard value;

[0169] Zone 2 (Smooth Driving): If the acceleration or braking frequency is between 3 and 5 times per minute, it is a normal and smooth driving style. The second correction ratio corresponding to this zone is 18%, which means 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 normal, with some acceleration and deceleration operations. In this case, the mapping table stipulates that the second correction ratio is 25%;

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

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

[0173] The system counts the number of accelerations and brakings in the last minute in real time, determines the zone it belongs to, and then extracts the corresponding second correction ratio from the driving behavior correction map. For example, if the system detects seven accelerations and brakings in a given minute, it falls into Zone 3 (Normal Driving) and will use 25% as the second correction ratio.

[0174] In step 4011, onboard sensors collect real-time data on the slope variation during vehicle travel. Because the effect of slope on energy consumption is not uniformly linear (energy consumption increases slightly on small slopes but increases significantly on large slopes), a preset slope correction mapping table defines a nonlinear relationship between different slope variation ranges and correction ratios.

[0175] This mapping table divides the slope change (unit: degrees) into multiple intervals:

[0176] Range 1 (almost flat): When the slope changes between 0 and 2 degrees, the road surface is almost flat and the vehicle's driving is basically unaffected by the slope. In this case, the slope correction mapping table specifies a corresponding third correction ratio of 5%, which means that this slight slope will only increase energy consumption by 5% based on the standard value.

[0177] Interval 2 (Small Slope): If the slope changes between 2-5 degrees, it is a small slope section. The third correction ratio corresponding to this interval is 12%, which means that a small slope will increase energy consumption by 12%;

[0178] Interval 3 (Medium Slope): When the slope changes between 5-8 degrees, the vehicle needs to overcome a certain amount of gravity when driving, which is a medium slope section. In this case, the mapping table stipulates that the third correction ratio is 22%;

[0179] Range 4 (High Slope): If the slope changes between 8-12 degrees, the vehicle becomes significantly more difficult to drive and energy consumption increases significantly. The third correction ratio corresponding to this range is 35%;

[0180] Zone 5 (Extreme Slope): When the slope changes by more than 12 degrees, the vehicle faces a huge challenge and consumes a lot of energy. In this case, the third correction ratio is set to 50%.

[0181] The system obtains the current slope change data in real time, determines the zone it belongs to, and then extracts the corresponding third correction ratio from the slope correction map. For example, if the slope change of the current road section is detected to be 9 degrees, it belongs to "Section 4 (High Slope)" and extracts 35% as the third correction ratio.

[0182] Step 402 first obtains average energy consumption data under a standard test environment. This is the average power consumption per kilometer of a vehicle tested under ideal, stable conditions, for example, 18Wh / km (Wh stands for Watt-hour, a unit of electrical energy). Adjustments are made to the average energy consumption data based on the first, second, and third correction ratios obtained in the previous three steps. The specific process is as follows:

[0183] To calculate the increased energy consumption due to vehicle dynamic stability, multiply the average energy consumption by the first correction factor. For example, if the average energy consumption is 18Wh / km and the first correction factor is 15%, the increased energy consumption due to vehicle dynamic stability = 18 × 15% = 2.7Wh / km.

[0184] To calculate the increased energy consumption due to driving behavior, multiply the average energy consumption by the second correction factor. Assuming the second correction factor is 25%, the increased energy consumption due to driving behavior = 18 × 25% = 4.5 Wh / km.

[0185] To calculate the increase in energy consumption due to slope, multiply the average energy consumption by the third correction factor. If the third correction factor 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 added values ​​to arrive at the user's personalized energy consumption baseline: Personalized Energy Consumption Baseline = 18 + 2.7 + 4.5 + 6.3 = 31.5 Wh / km. This value takes into account vehicle dynamic stability, user driving behavior, and environmental slope factors, more accurately reflecting the user's actual energy consumption level in the current driving scenario.

[0187] By integrating average energy consumption data with a dynamic energy consumption correction factor to generate a user-specific energy consumption baseline, this approach achieves highly accurate and personalized energy consumption assessments. Traditional energy consumption calculations are often based on standardized test data and fail 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 slope to generate unique energy consumption baselines for different users and driving scenarios, making energy consumption assessments more realistic.

[0188] Secondly, the reliability of range predictions has been improved. Accurate, personalized energy consumption baselines are the key foundation for range calculations. The resulting range recommendations provide users with a clearer and more accurate understanding of the vehicle's remaining driving capacity in their current usage scenario, effectively alleviating their "range anxiety" and avoiding trip interruptions caused by misjudgments of range.

[0189] In a preferred embodiment of the present invention, step 5 above, dynamically generating a personalized range recommendation value for the user's current usage scenario based on the personalized energy consumption baseline value and the battery capacity, and providing real-time feedback to the user via the vehicle terminal, may include:

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

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

[0192] Step 502: Obtain the slope variation, driving speed fluctuation, and start-stop frequency in the current driving scenario in real time, and modify the initial theoretical cruising range in real time using preset dynamic adjustment rules to generate a personalized cruising range recommendation for the user's current scenario. Specifically, the following steps are performed:

[0193] Step 5020: Based on the slope variation in the current driving scenario, a slope reduction coefficient corresponding to the slope variation is determined using a preset slope reduction mapping table; the slope reduction mapping table defines the correspondence between different slope 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 using a preset speed compensation mapping table; the speed compensation mapping table defines the correspondence between the speed fluctuation standard deviation range 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 start-stop frequency range and the loss ratio per unit time.

[0194] Step 5021 , adding the slope reduction coefficient, speed compensation coefficient, and start-stop loss coefficient to the initial theoretical cruising range to generate a personalized cruising range recommendation value for the user's current scenario;

[0195] Step 503: Update the personalized cruising range recommendation value in real time and feed it back to the user through the display module of the vehicle terminal.

[0196] In this embodiment of the present 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 in a specific usage scenario, which is obtained by comprehensively considering factors such as vehicle dynamic stability, driving behavior, and slope.

[0197] The standard test mileage is a fixed reference value, set at 1 kilometer, used to standardize energy consumption standards. The personalized energy consumption baseline value is calculated by comparing it with the standard test mileage to obtain the theoretical energy consumption per kilometer. For example, if the 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 / kilometer. This value represents the estimated amount of electricity consumed per kilometer of the vehicle 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 remaining available battery power, also in watt-hours (Wh). The current battery capacity is then divided by the theoretical energy consumption per kilometer to obtain the initial theoretical range.

[0199] For example, if the current battery capacity is 300Wh and the theoretical energy consumption per kilometer is 30Wh / km, then the initial theoretical range = 300Wh ÷ 30Wh / km = 10km. This 10km represents the theoretical distance the vehicle can still travel based on the current energy consumption level and the remaining battery charge, ignoring other real-time changing factors.

[0200] Step 5020: Determine the slope reduction coefficient:

[0201] The on-board sensors are used to collect data on the slope change amplitude in the current driving scene in real time. For example, the slope of the current driving section is 8 degrees. The system has a preset slope reduction mapping table, which divides the slope range and sets a corresponding reduction ratio for each range. For example, the reduction ratio for a slope of 0-3 degrees is 5%, 3-6 degrees corresponds to 10%, 6-9 degrees corresponds to 15%, and 9 degrees and above corresponds to 20%. The collected slope data is compared with the range in the mapping table. When the slope is detected to be 8 degrees, it is determined that it belongs to the 6-9 degree range, and the corresponding reduction ratio of 15% is extracted as the slope reduction coefficient. This means that when driving on a road section with a slope of 8 degrees, the vehicle's cruising range will be reduced by 15% due to the slope factor.

[0202] Speed ​​compensation coefficient determination:

[0203] The system continuously monitors vehicle speed fluctuations and measures the degree of speed fluctuation by calculating the standard deviation of speed 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 possible 3% increase in range), 3-6 km / h corresponds to 0%, 6-9 km / h corresponds to -5% (indicating large 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 that it falls within the 6-9 km / h range, and extracts -5% as the speed compensation factor.

[0204] Determination of start-stop loss coefficient:

[0205] Real-time statistics are collected on the number of starts and stops within a unit of time (e.g., 10 minutes). Assume that the vehicle started and stopped 12 times in the last 10 minutes, or 1.2 times per minute. A start-stop loss mapping table defines the relationship between the range of starts and stops per unit of time and the loss ratio. For example, a start-stop frequency of 0-0.5 times per minute corresponds to a loss ratio of 3%, 0.5-1 times per minute corresponds to a loss ratio of 6%, 1-1.5 times per minute corresponds to a loss ratio of 10%, and 1.5 times or more corresponds to a loss ratio of 15%. The system compares the counted start-stop frequency of 1.2 times per minute with the mapping table, determines that it falls within the 1-1.5 times range, and extracts 10% as the start-stop loss coefficient.

[0206] In step 5021, the slope reduction coefficient, speed compensation coefficient, and start-stop loss coefficient determined in step 5020 are combined with the initial theoretical cruising range obtained in step 501. The specific calculation method is to multiply the initial theoretical cruising range by each coefficient (note the positive and negative coefficients; a positive coefficient indicates an increase in cruising range, while a negative coefficient indicates a decrease in cruising range), and then add these results to the initial theoretical cruising range.

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

[0208] Mileage reduced due to slope = 10 km × 15% = 1.5 km;

[0209] Mileage lost due to speed fluctuation = 10 km × 5% = 0.5 km;

[0210] Mileage reduced due to start-stop loss = 10 km × 10% = 1 km;

[0211] Personalized range recommendation = 10 kilometers 1.5 km 0.5 km 1 km = 7 kilometers.

[0212] Step 503 sends the calculated personalized range recommendation to the display module of the vehicle terminal. The vehicle terminal is typically the vehicle's instrument panel or central control display. The display module updates and displays the recommended range in real time using clear and intuitive display formats (e.g., numbers, progress bars, etc.). This allows users to view the vehicle's remaining range at any time while driving, facilitating trip planning and optimally arranging charging times.

[0213] This method improves the accuracy and practicality of range predictions. Traditional range estimates often ignore complex factors in actual driving, leading to inaccurate range predictions and range anxiety. This method, however, comprehensively considers personalized energy consumption benchmarks as well as real-time scenario factors such as slope, speed fluctuations, and start-stop frequency. It provides users with range data tailored to actual driving conditions, helping them more effectively plan their travel routes and charging schedules, and reducing travel uncertainty.

[0214] In terms of vehicle intelligence, a 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. By observing the changes in the recommended range value, users can intuitively understand the impact of different driving styles and road conditions on vehicle energy consumption, allowing them to consciously adjust their driving habits and achieve energy-saving driving.

[0215] like Figure 2 As shown, an embodiment of the present invention also provides a personalized range recommendation system for battery motorcycle users, including:

[0216] Basic parameter acquisition module, used to obtain the battery capacity, motor power and average energy consumption data of the battery motorcycle under standard test conditions;

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

[0218] The dynamic relationship analysis module analyzes the changing characteristics of the dynamic spatial relationship between the battery pack center position, motor installation position, and vehicle center of gravity, including relative displacement and distribution stability parameters. It also generates a dynamic energy consumption correction factor based on the acceleration or braking frequency in the user's driving behavior and the slope variation amplitude in the environmental energy consumption influencing factors.

[0219] Energy consumption benchmark generation module, used to integrate average energy consumption data with dynamic energy consumption correction coefficient to obtain user-personalized energy consumption benchmark value;

[0220] The cruising range recommendation module is used to dynamically generate personalized cruising range recommendation values ​​for the user's current usage scenario based on personalized energy consumption benchmark values ​​and battery capacity, and provide real-time feedback to the user through the in-vehicle terminal.

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

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

[0223] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute the above-described method. All implementations in the above-described method embodiment are applicable to this embodiment and can achieve the same technical effects.

[0224] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for recommending personalized cruising range for battery motorcycle users, characterized in that: The method comprises: Step 1: Obtain basic parameters of the battery motorcycle, including battery capacity, motor power, and average energy consumption data under standard test conditions; Step 2: Based on the basic parameters, the user's actual driving data is collected in real time. The actual driving data includes the type of driving road conditions, slope changes, driving speed fluctuations, start and stop frequency, acceleration or braking behavior, and the real-time coordinates of the battery pack center position, motor installation position, and vehicle center of gravity position of the battery motorcycle detected in real time by on-board sensors; Step 3: Analyze the changing characteristics of the dynamic spatial relationship between the battery pack center position, motor installation position, and vehicle center of gravity position, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in the user's driving behavior and the slope change amplitude in the environmental energy consumption influencing factors, a dynamic energy consumption correction coefficient is generated. Step 4: The average energy consumption data is integrated 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 benchmark value and battery capacity, dynamically generate a personalized range recommendation value for the user's current usage scenario, and provide real-time feedback to the user through the in-vehicle terminal.

2. The method for recommending personalized cruising range for battery motorcycle users according to claim 1, characterized in that: Analyze the changing characteristics of the dynamic spatial relationship between the battery pack center position, motor installation position, and vehicle center of gravity, including relative displacement and distribution stability parameters. Combined with the acceleration or braking frequency in the user's driving behavior and the slope change amplitude in the environmental energy consumption influencing factors, generate a dynamic energy consumption correction factor, including: Based on the real-time coordinates of the battery pack center position, the motor installation position, and the vehicle center of gravity, respectively calculate a first real-time displacement vector between the battery pack center position and the motor installation position, a second real-time displacement vector between the motor installation position and the vehicle center of gravity position, and a third real-time displacement vector between the vehicle center of gravity position and the battery pack center position; Calculate the displacement fluctuation amplitudes of the battery pack center position, the motor installation position, and the vehicle center of gravity within a preset time window based on the real-time change trends of the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, and generate a distribution stability parameter; Based on the distribution stability parameter and the acceleration or braking frequency in the user's driving behavior, the first impact weight of driving behavior on energy consumption is quantified. In addition, the second impact weight of slope on energy consumption is quantified based on the slope change amplitude in the environmental energy consumption influencing factors. The quantified driving behavior impact weight and slope additional weight are obtained. The displacement fluctuation amplitude, distribution stability parameter, quantified driving behavior influence weight and slope additional weight are integrated to generate a dynamic energy consumption correction coefficient.

3. The method for recommending personalized cruising range for battery motorcycle users according to claim 2, characterized in that: Based on the real-time change trends of the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, the displacement fluctuation amplitudes of the battery pack center position, the motor installation position, and the vehicle center of gravity position within a preset time window are calculated to generate distribution stability parameters, including: Based on the first real-time displacement vector, the second real-time displacement vector, and the third real-time displacement vector, respectively extracting the displacement of each displacement vector at each sampling point within a preset time window, and counting the maximum displacement, minimum displacement, and average displacement within the time window; For each displacement vector, the displacement range within the time window is calculated based on the absolute difference between the maximum displacement and the minimum displacement. The absolute deviation between the displacement of each sampling point and the reference value is calculated using the average displacement as the reference value. The absolute deviation of all sampling points in the time window is averaged to obtain the deviation amplitude. 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 comprehensive displacement fluctuation amplitudes of the three displacement vectors are normalized to generate the distributed stability parameters that characterize the dynamic stability of the vehicle.

4. The method for recommending personalized cruising range for battery motorcycle users according to claim 3, characterized in that: The average energy consumption data is combined with the dynamic energy consumption correction coefficient to obtain the user's personalized energy consumption benchmark value, including: Based on the distribution stability parameter, determining a first correction ratio of the vehicle dynamic stability to energy consumption through a preset stability correction mapping table; According to the first correction ratio, combined with the driving behavior influence weight and the slope additional weight, the second correction ratio of driving behavior to energy consumption and the third correction ratio of slope to energy consumption are quantified using the driving behavior correction mapping table and the slope correction mapping table respectively; 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.

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

6. The method for recommending personalized cruising range for battery motorcycle users according to claim 5, characterized in that: Based on the personalized energy consumption baseline and battery capacity, a personalized range recommendation is dynamically generated for the user's current usage scenario and provided to the user in real time via the in-vehicle terminal, including: Calculate the theoretical energy consumption per kilometer based on the user's personalized energy consumption benchmark value, where the theoretical energy consumption is the ratio of the personalized energy consumption benchmark value to the standard test mileage; Generate the initial theoretical range based on the ratio of the user's current battery capacity to the theoretical energy consumption per kilometer; Real-time acquisition of slope changes, driving speed fluctuations, and start-stop frequency in the current driving scenario. Using preset dynamic adjustment rules, the initial theoretical range is corrected in real time to generate a personalized range recommendation for the user's current scenario. The personalized range recommendation value is updated in real time and fed back to the user through the display module of the vehicle terminal.

7. The method for recommending personalized cruising range for battery motorcycle users according to claim 6, characterized in that: The system obtains the slope change, driving speed fluctuation, and start-stop frequency in the current driving scenario in real time, and uses preset dynamic adjustment rules to make real-time corrections to the initial theoretical range to generate a personalized range recommendation for the user's current scenario, including: Based on the slope variation in the current driving scenario, a slope reduction coefficient corresponding to the slope variation is determined using a preset slope reduction mapping table; the slope reduction mapping table defines the correspondence between different slope 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 using a preset speed compensation mapping table; the speed compensation mapping table defines the correspondence between the speed fluctuation standard deviation range 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 start-stop frequency range and the loss ratio per unit time. The slope reduction coefficient, speed compensation coefficient and start-stop loss coefficient are added to the initial theoretical cruising range to generate a personalized cruising range recommendation value for the user's current scenario.

8. A personalized range recommendation system for battery motorcycle users, the system implementing the method according to any one of claims 1 to 7, characterized in that: include: Basic parameter acquisition module, used to obtain the battery capacity, motor power and average energy consumption data of the battery motorcycle under standard test conditions; The driving data acquisition module is used to collect the user's actual driving data such as the type of road condition, slope changes, driving speed fluctuations, start-stop frequency, acceleration or braking behavior in real time based on basic parameters. It also uses on-board sensors to detect the real-time coordinates of the battery pack center position, motor installation position and vehicle center of gravity position of the battery motorcycle in real time; The dynamic relationship analysis module analyzes the changing characteristics of the dynamic spatial relationship between the battery pack center position, motor installation position, and vehicle center of gravity, including relative displacement and distribution stability parameters. It also generates a dynamic energy consumption correction factor based on the acceleration or braking frequency in the user's driving behavior and the slope variation amplitude in the environmental energy consumption influencing factors. Energy consumption benchmark generation module, used to integrate average energy consumption data with dynamic energy consumption correction coefficient to obtain user-personalized energy consumption benchmark value; The cruising range recommendation module is used to dynamically generate personalized cruising range recommendation values ​​for the user's current usage scenario based on personalized energy consumption benchmark values ​​and battery capacity, and provide real-time feedback to the user through the in-vehicle terminal.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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