Electric bicycle endurance mileage evaluation method and system comprehensively considering seasonal changes and riding habits, medium and processor

Abstract

The invention discloses an electric bicycle endurance mileage evaluation method and system comprehensively considering seasonal changes and riding habits, a medium and a processor. According to the method, environment temperature, battery temperature, riding state data, date information, user historical riding data and battery parameters are collected and preprocessed, a battery performance model is established in combination with seasonal characteristics and temperature data, the user historical riding data are analyzed, a user riding habit model is established, and then the endurance mileage is estimated. The invention further provides a corresponding system, a computer readable storage medium and a processor. Compared with the prior art, the electric quantity and endurance mileage calculation parameters can be automatically adjusted according to season and temperature changes, meanwhile, the comprehensive influence of factors such as the riding habit of a user, the road condition and the load on electric quantity consumption is fully considered, the accuracy of electric quantity and endurance mileage display is remarkably improved, and the use experience of the electric bicycle is optimized.

Description

Technical Field

[0001] This invention relates to the field of electric bicycle mileage calculation technology, and in particular to a method, system, medium, and processor for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits. Background Technology

[0002] Electric bicycles, as an environmentally friendly and economical mode of transportation, have been widely used for daily commuting. However, the range of electric bicycles is affected by a variety of factors, especially temperature, which has a significant impact on battery performance. In low-temperature environments, the chemical reaction rate of the battery decreases, and the internal resistance increases, resulting in a reduction in usable power. While in high-temperature environments, although battery activity increases, excessively high temperatures can still lead to a decline in battery performance and a shortened lifespan.

[0003] Existing electric bicycle battery and range display systems typically use fixed calculation methods, failing to automatically adjust these parameters based on seasonal and temperature changes. This results in inflated battery and range readings in winter (indicating insufficient actual range) and inflated readings in summer (wasting battery potential). Furthermore, current technology does not adequately consider the combined impact of user riding habits, road conditions, and load on battery consumption.

[0004] Therefore, there is an urgent need for a system and method that can automatically adjust the calculation parameters of battery level and range by taking into account various factors such as seasonal temperature changes and user riding habits, so as to improve the accuracy of battery level and range display and optimize the user experience of electric bicycles.

[0005] Therefore, there is a need for a method, system, medium, and processor for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits. Summary of the Invention

[0006] To address the inaccurate calculation of battery level and range in existing technologies for electric bicycles, this application provides a method, system, medium, and processor for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits. This improves the accuracy of battery level and range display and optimizes the user experience of electric bicycles. The specific technical solution is as follows: A method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits includes: S1: Collect ambient temperature, battery temperature, riding status data, date information, user's historical riding data, and battery parameters, and perform preprocessing; S2: Determine the characteristics of the current season based on temperature data and date information; S3: A battery performance model is established by combining seasonal characteristics and temperature data to calculate the actual usable power. S4: Analyze users' historical cycling data and build a user cycling habit model to predict power consumption per unit distance; S5: Estimates the range based on the battery performance model, the user's riding habits model, and the current riding status.

[0007] Furthermore, the battery performance model formula is as follows: ; In the above formula, Temperature coefficient; Battery health rating; This represents the actual available power consumption. Remaining battery level; This is the load factor.

[0008] Furthermore, the formula for determining the temperature coefficient is as follows: in, This is the seasonal baseline coefficient; This refers to the ambient temperature sensitivity coefficient. This refers to the battery temperature sensitivity coefficient. The current ambient temperature; Current battery temperature; This serves as the reference temperature for battery performance.

[0009] Furthermore, the formula for determining the battery health coefficient is as follows: ; In the above formula, For the first Historical ambient temperature of each charge-discharge cycle; This serves as a reference temperature for battery performance. For the first Seasonal weighting coefficients for the next cycle; This represents the cumulative number of charge / discharge cycles. This refers to the cumulative battery usage time. For battery design life; This refers to the temperature aging sensitivity coefficient. This represents the aging factor over time.

[0010] Furthermore, the formula for determining the load factor is as follows: ; In the above formula, This represents the current average riding speed; Standard cruising speed; The average acceleration per unit time; This is the standard acceleration reference value; This represents the current actual load weight. Standard load weight; The road bump factor; These are the weighting coefficients.

[0011] Furthermore, in step S4, the analysis of users' historical cycling data and the establishment of a user cycling habit model for predicting power consumption per unit mileage include the following steps: S41: Establish a multiple regression model based on user cycling characteristics to parameterize user cycling habits and obtain user habit correction coefficients. ; S42: Determine the environmental impact coefficient based on environmental characteristics; S43: Combines a multiple regression model with a basic power consumption model to form a user riding habit model, used to calculate the user's power consumption per unit distance. ; S44: Determine the environmental correction factor based on the user's historical cycling data. and user habit correction factor The weighting coefficients of each parameter.

[0012] Furthermore, the user habit correction coefficient The formula for determining it is as follows: ; In the above formula, The user's current average riding speed; This is the standard cruising speed for electric bicycles; This is the speed weighting coefficient; The number of accelerations per unit time; To accelerate the weighting coefficients; This represents the average gradient of the current route. This is the slope weighting coefficient; The percentage of cycling during peak hours; The time period weighting coefficient; This represents the current actual load weight. Standard load weight; This is the load weighting coefficient.

[0013] An electric bicycle range assessment system that comprehensively considers seasonal changes and riding habits, applied to the above-mentioned electric bicycle range assessment method that comprehensively considers seasonal changes and riding habits, includes: The data acquisition module is used to collect ambient temperature, battery temperature, riding status data, date information, user historical riding data, and battery parameters, and to preprocess them. The judgment module is used to determine the characteristics of the current season based on temperature data and date information; The calculation module is used to build a battery performance model by combining seasonal characteristics and temperature data, and is used to calculate the actual usable power. The analysis module is used to analyze users' historical cycling data, build user cycling habit models, and predict power consumption per unit distance. The estimation module is used to estimate the driving range based on the battery performance model, the user's riding habits model, and the current riding status.

[0014] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the above-described method for evaluating the range of an electric bicycle that comprehensively considers seasonal changes and riding habits.

[0015] A processor for running a program, wherein the program executes the above-described method for evaluating the range of electric bicycles, which comprehensively considers seasonal changes and riding habits.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By collecting data such as ambient temperature, battery temperature, and riding status in real time, and combining historical riding data with seasonal characteristics, the system automatically adjusts the calculation parameters for battery level and range, improving the accuracy of battery level and range display and optimizing the user riding experience. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0018] Figure 1 A schematic diagram of a method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits; Figure 2 This is a schematic diagram of an electric bicycle range assessment system that comprehensively considers seasonal changes and riding habits. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be understood that, when used in this application, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term “and / or” as used in this application refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0023] Example 1 like Figure 1 The diagram shows a flowchart of a method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits, including the following steps: S1: Collects ambient temperature, battery temperature, riding status data, date information, user's historical riding data, and battery parameters, and performs preprocessing.

[0024] In practice, an ambient temperature sensor is mounted on the electric bicycle frame, away from the heat sources generated by the motor and battery, to collect real-time ambient temperature data. A battery temperature sensor is directly mounted on or inside the battery to collect real-time battery temperature data. Furthermore, temperature data is collected every 30 seconds with an accuracy of 0.5°C.

[0025] In specific implementation, the riding status data includes riding speed, acceleration, load weight, and road condition data. Specifically, a speed sensor is installed at the wheel to collect real-time riding speed; an acceleration sensor is used to detect acceleration and its changes to determine riding stability; and a weight sensor is installed at the connection between the seat and frame to detect load weight. Furthermore, the road condition data includes vehicle vibration frequency and amplitude data, and a road condition recognition unit identifies road conditions by analyzing the vehicle vibration frequency and amplitude.

[0026] Furthermore, the riding status data is collected once per second.

[0027] In practice, battery parameters include key parameters such as battery voltage, current, and internal resistance. A high-precision voltage and current detection chip is used to accurately measure battery parameters under different load conditions. Battery parameter data is collected every 5 seconds.

[0028] In practice, the preprocessing includes data filtering, outlier handling, etc., to ensure the accuracy and reliability of the data.

[0029] S2: Determine the characteristics of the current season based on temperature data and date information.

[0030] S21: Considering the calendar season, determine the current actual seasonal characteristics based on the average temperature and temperature change trend over the past 7 days. Specifically: Calculate the average temperature over the past 7 days (Average temperature varies with the season); Calculate the temperature change trend ΔT over the past 3 days (to determine early spring, late winter, and early summer). Combine the month information (Month) with the system date; Determine the seasonal characteristics (Season) based on the judgment rules; The specific implementation procedures are as follows: If Month falls within the range [12, 1, 2]: if Tavg<-5: Season = "Severe Winter" elif Tavg<5: Season = "Early Winter" else: if ΔT>0: Season = "Early Spring" else: Season = "Late Winter" elif Month in [3, 4, 5]: if Tavg<10: Season = "Early Spring" elif Tavg<20: Season = "Late Spring" else: Season = "early summer" elif Month in [6, 7, 8]: if Tavg<22: Season = "early summer" elif Tavg<28: Season = "Midsummer" else: if ΔT>0: Season = "Midsummer" else: Season = "Late Summer" elif Month in [9, 10, 11]: if Tavg>25: Season = "Late Summer" elif Tavg>15: Season = "Early Autumn" elif Tavg>5: Season = "Late Autumn" else: if ΔT<0: Season = "Early Winter" else: Season = "Late Autumn" The seasonal characteristics are further subdivided into more refined stages such as early spring, late spring, early summer, midsummer, early autumn, late autumn, early winter, and severe winter, with each stage corresponding to a different set of battery performance parameters.

[0031] S3: A battery performance model is established by combining seasonal characteristics and temperature data to calculate the actual usable power.

[0032] Based on the principles of battery electrochemistry, a temperature-battery performance mapping model is established. The actual usable capacity is calculated based on the battery's actual performance under current temperature conditions, combined with battery parameters and load conditions, using the following formula: ; ; ; ; In the above formula, Temperature coefficient; Battery health rating; Ambient temperature; N represents battery temperature; N represents the number of charge / discharge cycles; t represents usage time. Historical temperature data; This represents the actual available power. Remaining battery level; is the load factor; v is the velocity, a is the acceleration, m is the weight, and R is the road condition. This is the comprehensive capacity coefficient.

[0033] In practice, the remaining power This refers to the actual electrical energy currently stored in the battery, representing the amount of electrical energy reserves available for use at a specific time. In this solution, the calculation is based on the voltage measurement method, that is, the remaining power is determined according to the voltage-capacity mapping table of each manufacturer.

[0034] In practical implementation, temperature coefficient The calculation formula is seasonally adaptable: the seasonal characteristics provided by the seasonal judgment module affect the selection of the temperature coefficient calculation model, and the calculation formula is applicable to different seasonal temperature coefficients. The calculation parameters will be adjusted, for example: Winter (especially "severe winter" and "early winter"): The temperature coefficient is more sensitive to low temperatures, and the model will make a more conservative estimate of battery capacity; Summer (especially "midsummer" and "scorching heat"): The temperature coefficient is more sensitive to high temperatures, and the model will take into account the limiting effect of high temperatures on battery capacity; Transitional seasons (such as "late spring" and "early autumn"): Temperature coefficient calculations take into account the effects of temperature fluctuations.

[0035] in, This is the seasonal baseline coefficient (determined by seasonal characteristics); Environmental temperature sensitivity coefficient (unit: ); Battery temperature sensitivity coefficient (unit: ); The current ambient temperature ( (from an ambient temperature sensor); Current battery temperature ( (from the battery temperature sensor); The battery performance reference temperature ( As specified by the manufacturer, default ).

[0036] In practice, the battery health coefficient is calculated. Historical temperature data The system will perform weighted processing based on seasonal characteristics, and will also record battery performance changes in different seasons, storing this historical seasonal performance data. Incorporate this into the calculation of battery health coefficient.

[0037] For example, if battery performance has declined significantly in the past few "severe winter" seasons, the system will make a more conservative adjustment to the battery health coefficient when judging the current "severe winter".

[0038] / / Battery health rating takes into account seasonal characteristics and historical performance: ; In the above formula, For the first Historical ambient temperature (°C) for each charge-discharge cycle; The reference temperature (°C) for battery performance is specified by the manufacturer (e.g., 25°C). For the first The seasonal weighting coefficient for the next cycle is determined based on seasonal characteristics (such as "severe winter"). ,"midsummer" Dynamically assigned values ​​reflect the accelerating effect of seasons on aging; This represents the cumulative number of charge-discharge cycles. This refers to the cumulative battery usage time (in hours). Battery design life (in hours) is provided by the manufacturer; This is the temperature aging sensitivity coefficient (dimensionless), which needs to be calibrated experimentally (e.g., 0.005). This is the time aging factor (dimensionless), reflecting the natural aging rate (e.g., 0.1).

[0039] In practical implementation, load factor The calculation formula adjusts the parameter weights according to seasonal characteristics. For example, in the "hot summer" season, the same speed and acceleration will have a greater impact on the battery load than in the "late spring" season; in the "severe winter" season, the system will pay more attention to the additional battery consumption caused by climbing and acceleration.

[0040] ; In the above formula, The current average riding speed (km / h, from the speed sensor); Standard cruising speed (km / h, document default 20 km / h); The average acceleration per unit time (m / (from the accelerometer); Standard acceleration reference value (m / The value is calibrated by the system and the default value is 0.5 m / ; The current actual load weight (kg, from the weight sensor, including passengers and cargo); Standard load weight (kg, document default 75 kg); The road bump coefficient (dimensionless, derived from the road condition recognition unit, 0 indicates a smooth road surface, and 1 indicates an extremely bumpy road surface); The weighting coefficients (dimensionless) need to be determined through regression fitting of historical data.

[0041] / / The impact of seasonal characteristics on load factor: if (seasonal characteristic == "severe winter" || seasonal characteristic == "early winter") { Load factor = h( , * 1.2, * 1.1, ); / / Winter weighted average } else if (seasonal characteristic == "midsummer" || seasonal characteristic == "scorching heat") { Load factor = h( * 1.1, * 1.1, , ); / / Summer weighted } else { Load factor = h( , , , ); / / Normal season }

[0042] Based on seasonal characteristics, the battery performance model is dynamically adjusted to achieve more accurate power calculation and range prediction, fully reflecting the impact of seasonal characteristics on power calculation.

[0043] This dynamic adjustment mechanism based on seasonal characteristics is one of the core innovations of this application, which can provide more realistic power display and range prediction under different seasons and temperature conditions.

[0044] S4: Analyze users' historical cycling data to build a user cycling habit model for predicting power consumption per unit distance.

[0045] By recording and analyzing users' historical cycling data, including common speeds, acceleration frequencies, cycling distances, and cycling times, a user cycling habit model is established. This model is used to predict the user's potential energy consumption patterns under current cycling conditions, further improving the accuracy of energy consumption calculations. Specifically, the following steps are included: S41: Establish a multiple regression model based on user cycling characteristics to parameterize user cycling habits and obtain user habit correction coefficients. The formula is as follows: ; Base value: The formula uses "1" as the benchmark, and the correction coefficient fluctuates around the benchmark value (>1 indicates that the power consumption is higher than the standard value, and <1 indicates that it is lower than the standard value).

[0046] Adjustment factor for user habits; The user's current average riding speed (km / h); This is the standard cruising speed of an electric bicycle (km / h, usually taken as 20km / h). This is the speed weighting coefficient (default -0.015), which represents the impact on energy consumption when the speed deviates from the standard value by 1 km / h (the higher the speed, the larger the coefficient, and the higher the energy consumption). The number of accelerations per unit time (times / min); The acceleration weighting coefficient (default 0.03) reflects the additional energy consumption caused by frequent acceleration (the more frequent the acceleration, the larger the coefficient). The average gradient of the current route (%), positive for uphill and negative for downhill. This is the slope weighting coefficient (default 0.05). The steeper the slope (uphill), the larger the coefficient, and the energy consumption increases significantly. The percentage of cycling during peak hours (0~1, 1 indicates peak hours for all times); This is the time period weighting coefficient (default 0.08). During peak hours, energy consumption is higher than during off-peak hours due to frequent start-stop cycles. The actual load weight (kg); Standard load weight (kg, usually 75kg); This is the load weighting factor (default 0.002). Energy consumption increases slightly for every 1kg increase in load.

[0047] S42: Determine the environmental impact coefficient based on environmental characteristics, using the following formula: ; In the above formula, For the ambient temperature deviating from the reference value ( The weighting coefficients reflect the degree of influence of temperature on environmental corrections; The relative humidity deviates from the reference value ( The weighting coefficients reflect the influence of humidity. This is the weighting coefficient of the wind force influence coefficient W, used to quantify the corrective weight of wind force on energy consumption; The ambient temperature (°C, sourced from "Ambient Temperature Sensor" in the document). Relative humidity (%, which can be collected by the matching sensor, and the collection frequency is consistent with the riding status data); The wind force influence coefficient (-1 to 1, positive for headwind and negative for tailwind, obtained based on the extended function of the road condition recognition unit).

[0048] S43: Combines a multiple regression model with a basic power consumption model to form a user riding habit model, used to calculate the user's power consumption per unit distance. Power consumption per unit distance The formula is as follows: ; in, This is the environmental correction factor, which reflects the impact of environmental conditions (temperature, humidity, wind speed, etc.) on motor efficiency; The user habit correction coefficient reflects the impact of user riding habits (speed, acceleration mode, route selection, etc.) on energy consumption. It is obtained by parameterizing user riding characteristics using a multiple regression model. It is the basic unit of power consumption, which is automatically calculated based on the basic power consumption model of electric bicycles in winter and summer. It represents the power consumption of electric bicycles per kilometer under standard conditions (usually in Wh / km), and is obtained from historical data or manufacturer tests depending on the battery model. This refers to the power consumption per unit distance for the user.

[0049] S44: Determine the environmental correction factor based on the user's historical cycling data. and user habit correction factor The weighting coefficients of each parameter.

[0050] 1. Data Sample Construction Historical data collection scope: Extract the user's cycling data from the past 6 months (containing at least 50 valid cycling records), and each record must include: Environmental parameters: Ambient temperature Relative humidity (H) and wind force coefficient (W); Riding status parameters: average speed , speed up frequency ,slope Peak hours percentage Load weight ; Energy consumption benchmark data: The actual power consumption per ride (calculated based on real-time integration of battery current and voltage) and the corresponding mileage, yielding the actual power consumption per unit distance. .

[0051] Data preprocessing: Remove outliers (such as jump values ​​caused by data acquisition failures, or short-distance records with a riding time of less than 5 minutes); The data were grouped according to seasonal characteristics (such as severe winter and midsummer) to ensure sample balance under different environmental conditions.

[0052] 2. Weight Calculation Based on Multiple Linear Regression Adjusted by user habits and environmental correction factor Based on the joint model, the actual energy consumption data is fitted by the least squares method to solve for each weight coefficient.

[0053] Objective function: To achieve the predicted energy consumption per unit distance by the model. Compared with actual value The sum of squared errors is minimized, that is: ; Where n is the number of valid samples, and These are the environmental correction coefficient and the user habit correction coefficient for the i-th ride (including the weights to be determined).

[0054] Step-by-step solution process: (1) Environmental correction coefficient weight ( , , ): Control variables: Fix user habit parameters (e.g., adopt standard riding mode: constant speed 20km / h, no frequent acceleration, standard load 75kg), and only input historical data under different environmental conditions; Fitting calculation: This involves finding the weight values ​​that minimize the error in the impact of environmental factors on energy consumption using a regression model. For example: When energy consumption deviation is large in low-temperature environments, The absolute value will increase (enhanced temperature correction); users in the windy region Users in windless areas have a higher weighting than users in windless areas.

[0055] (2) User habit correction coefficient weight ( ~ ): Control variables: Fix environmental parameters (e.g., standard environment: 25℃, 50% humidity, no wind), and only input historical data of different cycling habits; Fitting calculation: For users' high-frequency cycling characteristics (such as frequent acceleration, frequent steep hill riding), the corresponding weights are amplified through a regression model, for example: Users who are used to rapid acceleration, (Acceleration weight) will be significantly higher for users who ride at a slower pace; Users who frequently ride up steep hills, (Slope weight) will increase.

[0056] 3. Dynamic optimization of weighting coefficients Real-time calibration: The system automatically re-collects cycling data from the past month every 30 days, repeats steps 1-2, and fine-tunes the weighting coefficients (adjustment range ≤10%) to adapt to the slow changes in users' cycling habits (such as changes in commuting routes or cycling styles).

[0057] Seasonal adaptation: For different seasonal characteristics (such as severe winter and midsummer), separate subsets of weight coefficients are trained, for example: In winter, (Ambient temperature weight) and (Slope weight) automatically switches to the winter optimized value (because the energy consumption for climbing is more significant at low temperatures). In summer, (Humidity weighting) increased (high temperature and high humidity have a greater impact on battery efficiency).

[0058] S5: Estimates the range based on the battery performance model, the user's riding habits model, and the current riding status.

[0059] S51: Determines the current power consumption per unit distance based on current riding status parameters and environmental parameters. This refers to the current consumption pattern.

[0060] Current riding status data includes: Environmental parameters: Ambient temperature Relative humidity (H) and wind force coefficient (W); Riding status parameters: average speed , speed up frequency ,slope Peak hours percentage Load weight .

[0061] S52: Taking into account both actual available battery power and current power consumption patterns, the predicted driving range is calculated as follows: ; Where S represents the driving range.

[0062] Furthermore, if the user uses the navigation function, and there is navigation route data, the system can predict the riding status parameters and environmental parameters of the navigation route and calculate the total range.

[0063] 1. Formula for calculating total driving range along navigation route: ; In the above formula, Total driving range for the navigation route (unit: km); This refers to the actual usable power calculated based on the battery performance model. The average power consumption per unit distance of the navigation path (unit: Wh / km, which needs to be calculated in conjunction with the path segmentation prediction results).

[0064] 2. Calculation of average power consumption per unit distance along the navigation route Navigation routes are divided by road segments (e.g.) , (Total number of road segments), first calculate the electricity consumption per unit mileage for each segment, then calculate the weighted average to obtain the total average for the entire route: ; Parameter description: : No. Length of each road segment (unit: km, taken from navigation route data); : No. Electricity consumption per unit mileage of road segment (unit: Wh / km).

[0065] 3. Calculation of power consumption per unit mileage for a single road segment No. The power consumption per unit distance of a road segment is related to the environmental parameters and riding status parameters of that segment, and the calculation formula is consistent with the user riding habit model in the document: Parameter description: Basic unit power consumption (unit: Wh / km); : No. Environmental correction factor for each road segment (calculated based on environmental parameters of the navigation path); : No. User habit correction factor for each segment of the route (calculated based on cycling status parameters of the navigation route and user history habits).

[0066] 4. Navigation Path Parameter Prediction Method Environmental parameters (used for calculation) : Obtain the real-time / forecast ambient temperature of each road segment from navigation data. relative humidity Wind force coefficient ; Substitute into the environmental correction factor formula: Riding status parameters (used for calculation) : Obtain the slope of each road segment from navigation data. Expected peak period percentage ; Predict the average speed of this road segment by combining historical user data. , speed up frequency Load weight ; Substitute into the user habit correction coefficient formula: S53: Dynamically adjusts the driving range. This includes the following steps: S531: The system continuously collects real-time data during riding; S532: Dynamically adjust the prediction model based on the deviation between the actual consumption rate and the predicted value; S533: Real-time updates and displays predicted driving range.

[0067] In this way, by integrating user habit models with real-time environmental data, the system can provide more accurate power consumption prediction per unit distance and range calculation, helping users to better plan riding routes and charging strategies.

[0068] S6: The system displays the calculation results to the user through the interactive display module and provides the following optimization suggestions based on the current situation: In low-temperature environments, it is recommended to preheat the battery or adjust the riding speed.

[0069] The innovation of this application lies in: 1. Innovatively transforming natural language descriptions of climate characteristics ("severe winter") into precise numerical parameters; 2. Breaking through the traditional single temperature-capacity mapping model, it introduces multidimensional analysis of temperature characteristics; 3. Innovatively, the battery capacity coefficient is decomposed into three independent yet mutually influential sub-coefficients: Temperature coefficient: reflects the effect of ambient temperature on battery performance; Battery health rating: reflects the impact of battery aging on capacity; Load factor: reflects the impact of actual load on available capacity; This decomposition and reconstruction method enables more accurate power prediction. 4. Innovatively integrates the physical and chemical properties of batteries with actual user scenarios.

[0070] The beneficial effects of this application are: 1. Improve power display accuracy: By taking seasonal temperature factors into account, the power calculation is dynamically adjusted to make the display closer to the actual available power. 2. Personalized User Experience: By learning users' riding habits, personalized power calculations and range estimates are provided for different users; 3. Extend battery life: By providing optimized riding suggestions, help users use the battery more effectively; 4. Improve travel safety: Reduce the risk of unexpected power outages due to inaccurate battery level displays; 5. High system scalability: Modular design makes the system easy to upgrade and expand.

[0071] Example 2 The difference between this embodiment and Embodiment 1 lies in the parameters and calculation method used for the user habit correction coefficient.

[0072] ; In the above formula, This represents the current average cycling speed (km / h). The standard cruising speed (20 km / h by default in the document) is used as the reference speed. The ratio between these values ​​reflects the degree to which the speed deviates from the standard value (>1 indicates higher speed and increased energy consumption). The maximum acceleration in a single event (m / s²) The product of acceleration frequency and intensity represents the number of accelerations per unit time (times / min). This represents the current load weight (kg, including people and cargo). For standard loads, their ratio directly reflects the linear impact of the load on energy consumption; The road bump factor is expressed as "1+". "This reflects the additional energy consumption caused by uneven road surfaces;" This represents the current daily cycling distance (km). The ratio represents the user's historical average daily distance (km), reflecting the deviation between the day's riding intensity and daily habits (long-distance riding may lead to a decrease in battery efficiency). This represents the percentage of time spent cycling at night. Since nighttime cycling increases energy consumption due to the use of lights, the higher the percentage, the greater the impact of this item.

[0073] The solution in this embodiment has the following effects: 1. More closely matches the nonlinear characteristics of energy consumption The relationship between the energy consumption of electric bicycles and factors such as speed, acceleration, and load is not simple linear, but rather exhibits a nonlinear correlation (for example, the impact of increased speed on energy consumption increases exponentially). Example 2 integrates the nonlinear relationships of various parameters through logarithmic transformation and outputs the data in exponential form, more accurately reflecting the actual energy consumption patterns.

[0074] For example: speed term It can reflect the nonlinear characteristics of "energy consumption surge during high-speed riding" and is more in line with physical laws than the speed weight coefficient of the linear model.

[0075] 2. Synergistic Expression of the Interaction of Multiple Factors In actual cycling, multiple factors (such as "acceleration frequency × intensity" and "load + bumpy road conditions") work synergistically to increase energy consumption. Example 2, by adding several terms and then exponentializing them, naturally incorporates the interactive effects of these factors: in the formula It directly reflects the "product effect of acceleration frequency and intensity", avoiding the complex calculations required to set interaction terms separately in linear models.

[0076] 3. Dynamic scaling of parameter influence The properties of the logarithmic function allow the influence weights of each parameter to be automatically adjusted in different intervals, which is more in line with real-world scenarios: At low speeds, speed changes have a relatively small impact on energy consumption, while at high speeds the impact is significant. Several factors can naturally amplify the correction range at high speeds. When the load weight is close to the standard value, the correction is gradual; when it far exceeds the standard value (such as when the load doubles), the correction magnitude increases rapidly, and there is no need to manually adjust the weight coefficient.

[0077] 4. Better scalability and adaptability New factors (such as the percentage of nighttime cycling and daily cycling distance) can be directly incorporated into the formula as logarithmic terms, without requiring a redesign of the overall model structure, thus facilitating expansion. For example The item can flexibly reflect the additional impact of lighting use on energy consumption without interfering with the calculation logic of other parameters.

[0078] 5. The intuitive interpretation of the correction coefficient Exponential output results It is always a positive number, and its value changes directly correspond to the energy consumption trend: This indicates that the actual energy consumption is higher than the standard value; the larger the value, the more significant the difference. The sign of each logarithmic term can directly reflect the "promoting" (positive) or "inhibiting" (negative) effect of the factor on energy consumption, which facilitates model debugging and result analysis.

[0079] In summary, the calculation method in Example 2, through the optimization of the mathematical model, more accurately captures the nonlinear and interactive characteristics of cycling energy consumption, thereby improving the accuracy of power calculation and its adaptability to different scenarios.

[0080] Furthermore, in this embodiment, the temperature coefficient The calculation can also be implemented using the following code, which also reflects the influence of seasonal characteristics: / / Calculation of temperature coefficient adjusted based on seasonal characteristics switch(seasonal characteristics) { case "Severe Winter": Temperature coefficient = 0.7 + 0.01× More sensitive to low temperatures break case "early winter": case "Late Winter": Temperature coefficient = 0.75 + 0.015× ; break case "Early Spring": case "Late Autumn": Temperature coefficient = 0.8 + 0.018× ; break case "Late Spring": case "Early Autumn": Temperature coefficient = 0.9 + 0.02× ; break case "early summer": case "Late Summer": Temperature coefficient = 1.0 - 0.005×( - 20); break case "Midsummer": Temperature coefficient = 1.0 - 0.01×( - 25); / / More sensitive to high temperatures break case "scorching heat": Temperature coefficient = 0.95 - 0.015 × ( -30); / / High temperatures have a stronger limiting effect on battery capacity. break default: Temperature coefficient = 1.0; / / Normal temperature range }

[0081] Example 3 like Figure 2 The diagram shows an electric bicycle range assessment system that comprehensively considers seasonal changes and riding habits. Applied to the aforementioned electric bicycle range assessment method that comprehensively considers seasonal changes and riding habits, it includes: The data acquisition module is used to collect ambient temperature, battery temperature, riding status data, date information, user historical riding data, and battery parameters, and to preprocess them. The judgment module is used to determine the characteristics of the current season based on temperature data and date information; The calculation module is used to build a battery performance model by combining seasonal characteristics and temperature data, and is used to calculate the actual usable power. The analysis module is used to analyze users' historical cycling data, build user cycling habit models, and predict power consumption per unit distance. The estimation module is used to estimate the driving range based on the battery performance model, the user's riding habits model, and the current riding status.

[0082] Example 4 A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the above-described method for evaluating the range of an electric bicycle that comprehensively considers seasonal changes and riding habits.

[0083] Example 5 A processor for running a program, wherein the program executes the above-described method for evaluating the range of electric bicycles, which comprehensively considers seasonal changes and riding habits.

[0084] The workflow of this application in practical application is as follows: 1. When the user starts the electric bicycle, the system automatically detects that the current ambient temperature is -5℃ (winter) and the battery temperature is 0℃; 2. The season determination module identifies the current season as "severe winter"; 3. The power calculation module adjusts the battery capacity coefficient to 0.7 based on the characteristics of "severe winter" and temperature data; 4. The battery parameter acquisition module detects current battery voltage, current, and other parameters to determine that the battery charging status is 90%. 5. Based on a capacity factor of 0.7 and a state of charge of 90%, the actual usable power is calculated to be 63%. 6. The user habit learning module extracts the user's historical cycling data and finds that the user typically cycles at a speed of 20 km / h, with a daily commute of approximately 10 km on flat roads; the load factor is set to 1.0. 7. The system calculates an estimated driving range of 28 kilometers (40 kilometers under normal temperatures). 8. The display and interaction module shows the user "Battery level 63%, estimated range 28km" and prompts "Battery performance is reduced in low temperature environment, it is recommended to reduce speed to extend range"; 9. During the ride, the system continuously monitors the battery temperature. When the battery temperature rises to 10°C, it recalculates the capacity factor to 0.85, updates the actual usable power to 76.5%, and increases the estimated range to 34 kilometers. 10. After the user arrives at their destination, the system records various data points from the ride to optimize the user's riding habit model.

[0085] This application discloses a method, system, medium, and processor for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits. The method collects and preprocesses ambient temperature, battery temperature, riding status data, date information, user historical riding data, and battery parameters. It then establishes a battery performance model by combining seasonal characteristics and temperature data, and analyzes user historical riding data to establish a user riding habit model, thereby estimating the range. This invention also provides a corresponding system, computer-readable storage medium, and processor. Compared with existing technologies, this invention can automatically adjust the power consumption and range calculation parameters according to seasonal and temperature changes, while fully considering the comprehensive impact of user riding habits, road conditions, load, and other factors on power consumption. This significantly improves the accuracy of power consumption and range display and optimizes the user experience of electric bicycles.

[0086] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0087] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0088] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of this application.

Claims

1. A method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits, characterized in that, include: S1: Collect ambient temperature, battery temperature, riding status data, date information, user's historical riding data, and battery parameters, and perform preprocessing; S2: Determine the characteristics of the current season based on temperature data and date information; S3: A battery performance model is established by combining seasonal characteristics and temperature data to calculate the actual usable power. S4: Analyze users' historical cycling data and build a user cycling habit model to predict power consumption per unit distance; S5: Estimates the range based on the battery performance model, the user's riding habits model, and the current riding status.

2. The method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits according to claim 1, characterized in that, The battery performance model formula is as follows: ； In the above formula, Temperature coefficient; Battery health rating; This represents the actual available power consumption. Remaining battery level; This is the load factor.

3. The method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits according to claim 2, characterized in that, The formula for determining the temperature coefficient is as follows: in, This is the seasonal baseline coefficient; This is the ambient temperature sensitivity coefficient; This refers to the battery temperature sensitivity coefficient. The current ambient temperature; Current battery temperature; This serves as the reference temperature for battery performance.

4. The method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits according to claim 2, characterized in that, The formula for determining the battery health coefficient is as follows: ； In the above formula, For the first Historical ambient temperature of each charge-discharge cycle; This serves as a reference temperature for battery performance. For the first Seasonal weighting coefficients for the next cycle; This represents the cumulative number of charge / discharge cycles. This refers to the cumulative battery usage time. For battery design life; This refers to the temperature aging sensitivity coefficient. This represents the aging factor over time.

5. The method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits according to claim 2, characterized in that, The formula for determining the load factor is as follows: ； In the above formula, This represents the current average riding speed; Standard cruising speed; The average acceleration per unit time; This is the standard acceleration reference value; This represents the current actual load weight. Standard load weight; The road bump factor; These are the weighting coefficients.

6. The method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits according to claim 1, characterized in that, In step S4, the analysis of users' historical cycling data and the establishment of a user cycling habit model for predicting power consumption per unit mileage include the following steps: S41: Establish a multiple regression model based on user cycling characteristics to parameterize user cycling habits and obtain user habit correction coefficients. ; S42: Determine the environmental impact coefficient based on environmental characteristics; S43: Combines a multiple regression model with a basic power consumption model to form a user riding habit model, used to calculate the user's power consumption per unit distance. ; S44: Determine the environmental correction factor based on the user's historical cycling data. And user habit correction factor The weighting coefficients of each parameter.

7. The method for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits according to claim 6, characterized in that, The user habit correction coefficient The formula for determining it is as follows: ； In the above formula, The user's current average riding speed; This is the standard cruising speed for electric bicycles; This is the speed weighting coefficient; The number of accelerations per unit time; To accelerate the weighting coefficients; This represents the average gradient of the current route. This is the slope weighting coefficient; The percentage of cycling during peak hours; The time period weighting coefficient; This represents the current actual load weight. Standard load weight; This is the load weighting coefficient.

8. A system for evaluating the range of electric bicycles that comprehensively considers seasonal changes and riding habits, characterized in that, The electric bicycle range assessment method according to any one of claims 1 to 7, which comprehensively considers seasonal changes and riding habits, includes: The data acquisition module is used to collect ambient temperature, battery temperature, riding status data, date information, user historical riding data, and battery parameters, and to preprocess them. The judgment module is used to determine the characteristics of the current season based on temperature data and date information; The calculation module is used to build a battery performance model by combining seasonal characteristics and temperature data, and is used to calculate the actual usable power. The analysis module is used to analyze users' historical cycling data, build user cycling habit models, and predict power consumption per unit distance. The estimation module is used to estimate the driving range based on the battery performance model, the user's riding habits model, and the current riding status.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the electric bicycle range assessment method according to any one of claims 1 to 7, which comprehensively considers seasonal changes and riding habits.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the electric bicycle range evaluation method that comprehensively considers seasonal changes and riding habits as described in any one of claims 1 to 7.

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