Intelligent terminal-based electric vehicle endurance calculation method and system, and storage medium

Abstract

The application discloses a kind of based on intelligent terminal's electric vehicle endurance measurement method, system and storage medium, including by the state parameters of battery in electric vehicle in real time acquisition through BLE module, and transmission to intelligent terminal;The geographic position information of electric vehicle is obtained in real time by the positioning module of intelligent terminal, and the actual driving distance of each use of electric vehicle is calculated based on the geographic position information of electric vehicle;The change of the state parameters of battery during each time electric vehicle is used is associated with the actual driving distance of each use of electric vehicle Analysis, and based on the correlation analysis result, ride coefficient model is constructed in intelligent terminal;According to the current state parameters of the battery of electric vehicle and the ride coefficient model established, the remaining endurance driving range of electric vehicle is calculated and output;So that the measurement result of endurance range is more accurate and credible, and the precision of the remaining endurance driving range measured is higher.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, and in particular to a method for calculating the range of electric vehicles based on a smart terminal. Background Technology

[0002] The driving range of electric vehicles is one of the core indicators that users care about most. Currently, most two-wheeled electric vehicles on the market use a simple voltage method or coulomb method to estimate the remaining power and display it through several LED lights. This method has the following significant drawbacks: The display is extremely inaccurate; the battery power display is greatly affected by temperature, load, and battery aging. Typically, the first few bars of power last a long time, while the last bar depletes rapidly, making it impossible for users to obtain reliable information about the remaining range. It lacks personalization; the official driving range is based on test results under ideal conditions and does not consider the user's weight, riding habits, road conditions, or frequent start-stop cycles, leading to a significant discrepancy between the actual driving range and the advertised range. Finally, it lacks learning capabilities; existing systems cannot learn and correct themselves based on long-term user habits, and the estimation model remains fixed. Summary of the Invention

[0003] Purpose of the invention: In order to overcome the shortcomings of the existing technology, the present invention provides a method for calculating the range of electric vehicles based on smart terminals. The method constructs a riding coefficient model by using the state parameters of the battery in the electric vehicle and the actual driving distance of the electric vehicle, and calculates the remaining driving range of the electric vehicle based on the riding coefficient model.

[0004] Technical Solution: To achieve the above objectives, the present invention provides a method for calculating the range of an electric vehicle based on a smart terminal, comprising the following steps:

[0005] Step 1: The battery management module obtains the status parameters of the battery in the electric vehicle in real time, and the BLE communication module transmits the status parameters to the smart terminal processing module.

[0006] Step 2: Obtain the electric vehicle's geographical location information in real time through the positioning module of the smart terminal, and calculate the actual driving distance of the electric vehicle each time it is used based on the electric vehicle's geographical location information;

[0007] Step 3: The intelligent terminal processing module performs correlation analysis between the changes in the battery state parameters during each use of the electric vehicle and the actual driving distance of the electric vehicle each time, and builds a riding coefficient model on the intelligent terminal based on the correlation analysis results.

[0008] Step 4: Calculate and output the remaining driving range of the electric vehicle based on the current state parameters of the battery and the established riding coefficient model.

[0009] Furthermore, the state parameters of the battery in the electric vehicle include battery temperature, battery cycle count, battery SOC value, and battery health status (SOH). Based on the battery temperature and battery cycle count, a capacity compensation coefficient library for temperature and a capacity compensation coefficient library for cycle count are established. The capacity compensation coefficient library for temperature contains several capacity compensation coefficients for different temperatures, all calculated using the battery's standard capacity and the measured capacity of the battery at different temperatures. The calculation process is as follows:

[0010]

[0011] In the formula, α is the capacity compensation coefficient for temperature, C_MAX is the standard capacity of the battery, and C_T is the measured capacity of the battery at a certain temperature.

[0012] Furthermore, the capacity compensation coefficient library for the number of cycles contains several capacity compensation coefficients for different number of cycles. These capacity compensation coefficients for different number of cycles are calculated using the standard battery capacity and the measured capacity at different number of cycles. The calculation process is as follows:

[0013]

[0014] In the formula, β is the capacity compensation coefficient for the number of cycles, C_MAX is the standard capacity of the battery, and C_A is the measured capacity of the battery at a certain number of cycles.

[0015] Furthermore, in step three, the intelligent terminal processing module performs a correlation analysis between the changes in the battery's state parameters during each use of the electric vehicle and the actual driving distance of the electric vehicle in each use. The correlation analysis between the changes in the battery's state parameters during a specific use of the electric vehicle and the actual driving distance includes the following steps:

[0016] Step 1-1: Obtain the start and end SOC values ​​of the electric vehicle for this usage, and calculate the total power consumption. ;

[0017] Step 1-2: Obtain the real-time geographical location information of the electric vehicle during this use, and calculate the actual driving distance D of the electric vehicle during this use;

[0018] Steps 1-3: Calculate the temporary unit electricity driving coefficient Ki of the electric vehicle using the formula. The calculation process is as follows:

[0019]

[0020] In the formula, Ki is the temporary unit energy travel coefficient used by the electric vehicle this time, which is the temporary unit energy travel coefficient used by the electric vehicle for the i-th time; D is the actual travel distance of the electric vehicle this time. This represents the total power consumption of the electric vehicle during this use; SOH i The health status of the battery during the i-th use of the electric vehicle;

[0021] Steps 1-4: Correct the temporary unit energy driving coefficient Ki of the electric vehicle for this use by using the capacity compensation coefficient for the corresponding temperature and the capacity compensation coefficient for the number of cycles, and obtain the unit energy driving coefficient of the electric vehicle for this use. The calculation process is as follows:

[0022]

[0023] In the formula, Ki_corrected is the corrected unit power driving coefficient of the electric vehicle in this cycle, α is the capacity compensation coefficient for temperature, and β is the capacity compensation coefficient for the number of cycles.

[0024] Furthermore, the riding coefficient model is constructed on the smart terminal based on the correlation analysis results. The construction of the riding coefficient model includes an initial stage and a stable stage. A usage time threshold for electric vehicles is set. The time period from the time the electric vehicle is manufactured to the usage time threshold is set as the initial stage, and the time period after the usage time threshold is set as the stable stage.

[0025] In the initial stage, the unit energy travel coefficient of the electric vehicle is obtained for several uses, and the arithmetic mean of the unit energy travel coefficient is calculated. The arithmetic mean of the calculated unit energy travel coefficient is used as the riding coefficient KS_corrected. The riding coefficient KS_corrected is calculated and updated in real time every time period T1. The calculation process is as follows:

[0026]

[0027] In the formula, Ki_corrected is the corrected unit energy travel coefficient of the electric vehicle used for the i-th time, and i is the number of unit energy travel coefficients used by the electric vehicle.

[0028] Furthermore, during the stabilization phase, the unit energy travel coefficient of the electric vehicle is obtained several times. A weighted average method is used to assign weights to each unit energy travel coefficient in chronological order, from oldest to newest, to obtain the riding coefficient KD_corrected. The riding coefficient KD_corrected is calculated and updated in real time every T2 period. The calculation process is shown below:

[0029]

[0030] In the formula, Wi is the weight of the unit energy driving coefficient used by the electric vehicle in the i-th time, and i is the number of unit energy driving coefficients used by the electric vehicle.

[0031] Furthermore, when a user needs to check the current remaining driving range of an electric vehicle, the following steps are involved:

[0032] Step 2-1: Obtain the current battery status parameters in the electric vehicle in real time through the BLE module;

[0033] Step 2-2: Based on the battery temperature and battery cycle count in the current battery state parameters of the electric vehicle, query the corresponding capacity compensation coefficient α_current for temperature and the capacity compensation coefficient β_current for cycle count in the capacity compensation coefficient library for temperature and the capacity compensation coefficient library for cycle count.

[0034] Steps 2-3: Obtain the latest riding coefficient KS_corrected for the initial stage and the riding coefficient KD_corrected for the stable stage, and determine whether the electric vehicle is in the initial stage or the stable stage.

[0035] Steps 2-4: When the use of the electric vehicle is in its initial stage, a riding coefficient model is obtained based on the obtained riding coefficient. The calculation formula is as follows:

[0036]

[0037] In the formula, L1 is the remaining driving range in the initial stage, SOC is the current SOC value of the battery in the electric vehicle, and SOH is the current battery health status in the electric vehicle.

[0038] When the use of electric vehicles is in a stable phase, the calculation formula for the riding coefficient model based on the obtained riding coefficient is as follows:

[0039]

[0040] In the formula, L2 is the remaining driving range in the stable phase, SOC is the current SOC value of the battery in the electric vehicle, and SOH is the current battery health status of the electric vehicle.

[0041] Furthermore, an electric vehicle range calculation system based on a smart terminal is provided. This system is used to implement the aforementioned electric vehicle range calculation method based on a smart terminal, and includes:

[0042] Battery management module: used to acquire the state parameters of the battery in the electric vehicle, including battery temperature, battery cycle count, battery SOC value, and battery health status (SOH).

[0043] BLE communication module: Used to transmit the acquired battery status parameters in the electric vehicle to the smart terminal processing module;

[0044] Data storage module: Used to store the capacity compensation coefficient library for temperature and the capacity compensation coefficient library for cycle number, and also stores the calculated initial stage riding coefficient KS_corrected and stable stage riding coefficient KD_corrected;

[0045] GPS positioning module: used to obtain the geographical location information of the electric vehicle;

[0046] Intelligent terminal processing module: used to build a riding coefficient model and calculate the remaining driving range based on the riding coefficient model.

[0047] Furthermore, a storage medium contains an executable program, which, when executed by a processor, enables the aforementioned method for calculating the range of an electric vehicle based on a smart terminal.

[0048] Beneficial Effects: This invention provides a method for calculating the driving range of electric vehicles based on intelligent terminals. By integrating real GPS geographic displacement data with multi-dimensional parameters such as temperature and cycle count, it constructs a more reliable foundation for energy consumption analysis. It fundamentally avoids the serious errors caused by traditional solutions that rely solely on voltage estimation, making the driving range calculation results more accurate and reliable, and improving the accuracy of the calculation. The riding coefficient model can be dynamically updated and iteratively corrected based on long-term historical riding data. As the usage time increases, its prediction accuracy will become higher and higher, and it can automatically adapt to the trend of battery aging without manual intervention from the user, achieving a smart experience that becomes more accurate with use and realizing adaptive learning. Attached Figure Description

[0049] Figure 1 A flowchart of the cycling coefficient calculation process;

[0050] Figure 2 A flowchart illustrating the process of calculating the remaining driving range;

[0051] Figure 3 This is a block diagram of an electric vehicle range calculation system based on a smart terminal. Detailed Implementation

[0052] The invention will now be further described with reference to the accompanying drawings.

[0053] like Figure 1 As shown, the method for calculating the range of electric vehicles based on smart terminals includes the following steps:

[0054] Step 1: The battery management module obtains the status parameters of the battery in the electric vehicle in real time, and the BLE communication module transmits the status parameters to the smart terminal processing module.

[0055] Step 2: Obtain the electric vehicle's geographical location information in real time through the positioning module of the smart terminal, and calculate the actual driving distance of the electric vehicle each time it is used based on the electric vehicle's geographical location information;

[0056] Step 3: The intelligent terminal processing module performs correlation analysis on the changes in the battery state parameters during each use of the electric vehicle and the actual driving distance of the electric vehicle in each use, and constructs a riding coefficient model on the intelligent terminal based on the correlation analysis results; the riding coefficient model can be simply considered as representing the actual driving distance corresponding to a unit of electricity consumption.

[0057] Step 4: Calculate and output the remaining driving range of the electric vehicle based on the current state parameters of the battery and the established riding coefficient model.

[0058] The state parameters of the battery in the electric vehicle include battery temperature, battery cycle count, battery SOC value, and battery health status (SOH), with the SOH value ranging from 1 to 100. Based on the battery temperature and battery cycle count, a capacity compensation coefficient library for temperature and a capacity compensation coefficient library for cycle count are established. The temperature capacity compensation coefficient library contains several capacity compensation coefficients for different temperatures, all calculated using the battery's standard capacity and measured capacity at different temperatures. The calculation process is shown below:

[0059]

[0060] In the formula, α is the capacity compensation coefficient for temperature, C_MAX is the standard capacity of the battery, and C_T is the measured capacity of the battery at a certain temperature. Each temperature corresponds to a capacity compensation coefficient α. The measured capacity C_T of the battery at a certain temperature is obtained by placing the target battery at the corresponding temperature for capacity testing. When the target battery is tested for capacity, its battery health SOH is 100.

[0061] The capacity compensation coefficient library for the number of cycles contains capacity compensation coefficients for several number of cycles. These capacity compensation coefficients for several number of cycles are calculated based on the standard battery capacity and the measured capacity at different number of cycles. The calculation process is as follows:

[0062]

[0063] In the formula, β is the capacity compensation coefficient for the number of cycles, C_MAX is the standard capacity of the battery, and C_A is the measured capacity of the battery at a certain number of cycles. Each number of cycles corresponds to a capacity compensation coefficient β for the number of cycles, and the measured capacity C_A of the battery at a certain number of cycles is obtained by performing a cycle life test on the target battery and measuring the battery capacity at each cycle. When the target battery is subjected to a cycle life test, its health status (SOH) is 100 at the first cycle life test.

[0064] In step three, the intelligent terminal processing module performs a correlation analysis between the changes in the battery's state parameters during each use of the electric vehicle and the actual driving distance of the electric vehicle during each use. This involves correlating the changes in the battery's state parameters during the same period of electric vehicle use with the actual driving distance during that period. The correlation analysis between the changes in the battery's state parameters during a specific period of electric vehicle use and the actual driving distance includes the following steps:

[0065] Step 1-1: Obtain the start and end SOC values ​​of the electric vehicle for this usage, and calculate the total power consumption. For example, if the SOC value is 100% at the start of the ride and 77% at the end, then ΔS = 23%.

[0066] Step 1-2: Obtain the real-time geographical location information of the electric vehicle during this use, and calculate the actual driving distance D of the electric vehicle during this use;

[0067] Steps 1-3: Calculate the temporary unit electricity driving coefficient Ki of the electric vehicle using the formula. The calculation process is as follows:

[0068]

[0069] In the formula, Ki is the temporary unit power driving coefficient used by the electric vehicle in this use, which is the temporary unit power driving coefficient used by the electric vehicle for the i-th time; D is the actual driving distance of the electric vehicle in this use, which is the actual driving distance of the electric vehicle for the i-th time. This represents the total power consumption of the electric vehicle during this use, which is also the total power consumption of the electric vehicle during its i-th use; SOH i This represents the battery health status during the i-th use of the electric vehicle; the data is read from the battery management module, and the value ranges from 1 to 100; for example... Based on the original power consumption, the maximum battery capacity is 1000. When the battery level drops from 80% to 30%, it consumes 50% of the battery. Multiplying this by the battery health status (SOH) provides a more accurate estimate of the energy loss. The temporary unit energy travel coefficient Ki used in this electric vehicle calculation represents the number of kilometers traveled per 1% of battery consumption. For example, if the vehicle travels 10 kilometers, consumes 20% of the battery, and has a health status of 100, then Ki = 10 / (20 * 100 * 0.01) = 0.5 km / %.

[0070] Steps 1-4: Correct the temporary unit energy driving coefficient Ki of the electric vehicle for this use by using the capacity compensation coefficient for the corresponding temperature and the capacity compensation coefficient for the number of cycles, and obtain the unit energy driving coefficient of the electric vehicle for this use. The calculation process is as follows:

[0071]

[0072] In the formula, Ki_corrected is the corrected unit energy travel coefficient of the electric vehicle in this use; α is the capacity compensation coefficient for temperature, which is the capacity compensation coefficient for the temperature corresponding to the i-th use of the electric vehicle; β is the capacity compensation coefficient for the number of cycles, which is the capacity compensation coefficient for the number of cycles corresponding to the i-th use of the electric vehicle. When the electric vehicle is detected to start moving during this use, the unit energy travel coefficient of the electric vehicle in this use is calculated after the electric vehicle has stopped for a period of time T3 during this use. The time T3 can be 10 minutes.

[0073] The cycling coefficient model is constructed on the smart terminal based on the correlation analysis results. The construction of the cycling coefficient model includes an initial stage and a stable stage. A usage time threshold for electric vehicles is set. The time period from the time the electric vehicle is manufactured to the usage time threshold is set as the initial stage, and the time period after the usage time threshold is set as the stable stage. At this time, the usage time threshold for electric vehicles can be set as the time of the 5th use of the electric vehicle after it is manufactured. That is, the time from the time the electric vehicle is manufactured to the 5th use is set as the initial stage, and the time period after the 5th use is set as the stable stage.

[0074] In the initial stage, the unit energy travel coefficient of the electric vehicle is obtained for several uses, and the arithmetic mean of the unit energy travel coefficient is calculated. The arithmetic mean of the calculated unit energy travel coefficient is used as the riding coefficient KS_corrected. The riding coefficient KS_corrected is calculated and updated in real time every time period T1. The calculation process is as follows:

[0075]

[0076] In the formula, Ki_corrected is the corrected unit energy travel coefficient of the electric vehicle used for the i-th time, and i is the number of unit energy travel coefficients used by the electric vehicle.

[0077] During the stable phase, the unit energy travel coefficient of the electric vehicle is obtained from at least five instances. A weighted average method is then used to assign weights to each unit energy travel coefficient in chronological order, from oldest to most recent, to obtain the riding coefficient KD_corrected. Furthermore, the riding coefficient KD_corrected is calculated and updated in real-time every T2 interval. The calculation process is shown below:

[0078]

[0079] In the formula, Wi is the weight of the unit power driving coefficient of the electric vehicle used for the i-th time. When weighting, the weight of the unit power driving coefficient of the electric vehicle used recently can be increased to make the calculated riding coefficient closer to the user's current usage habits; i is the number of unit power driving coefficients used by the electric vehicle.

[0080] During the stable phase, the unit electricity driving coefficient of the electric vehicle is also obtained from several trips and calculated using a weighted average method. The weight Wi is calculated based on the riding timestamp, with a larger weight for timestamps that are more recent than the present. The specific calculation formula is as follows:

[0081]

[0082] In the formula, T_current is the current timestamp, which is the timestamp of the electric vehicle's current use; Ti is the timestamp of the end of the i-th use of the electric vehicle; Σ is the summation operator, which sums the difference between the current timestamp and the timestamp of the i-th use from 1 to i; k is the time decay factor (k≥0). The time decay factor k can be set according to actual needs. When k=0, it degenerates into an arithmetic mean; when k=1, it is linear decay; when k>1, recent data has a higher weight. For example, when k=2, the weight of the most recent ride is about 25 times that of the previous 5th ride.

[0083] In the initial and stable phases, the riding coefficient can be dynamically updated after each use of the electric vehicle. For example, after the current use of the electric vehicle, the unit power consumption travel coefficient of the current use and the unit power consumption travel coefficient of the previous several uses of the electric vehicle are obtained. Then, the riding coefficient KS_corrected in the initial phase and the riding coefficient KD_corrected in the stable phase are calculated by formula, thereby realizing real-time dynamic updates of the riding coefficient in the initial and stable phases.

[0084] In the initial stage, an arithmetic mean is used as the riding coefficient, primarily based on the following considerations: The data sample size is limited at this stage, typically representing the first 5-10 rides of the electric vehicle. The arithmetic mean can quickly establish a stable initial model baseline, avoiding excessive disturbance to the model due to individual abnormal riding data, ensuring that the system can provide relatively reliable range predictions from the initial stage of use. Simultaneously, user riding habits may not yet be stable in the initial stage, and using a simple arithmetic mean helps reduce model complexity. Once the system enters the stable stage, sufficient historical riding data has been accumulated. At this point, a weighted average method is used, assigning higher weights to recent data. This allows the riding coefficient model to more sensitively reflect changes in users' latest riding habits and the gradual degradation trend of battery performance, enabling continuous self-learning and optimization of the model. This phased design strategy balances the needs for initial model stability and long-term adaptability.

[0085] like Figure 2 As shown, when a user needs to check the current remaining driving range of an electric vehicle, the following steps are involved:

[0086] Step 2-1: Obtain the current battery status parameters in the electric vehicle in real time through the BLE module;

[0087] Step 2-2: Based on the battery temperature and battery cycle count in the current battery state parameters of the electric vehicle, query the corresponding capacity compensation coefficient α_current for temperature and the capacity compensation coefficient β_current for cycle count in the capacity compensation coefficient library for temperature and the capacity compensation coefficient library for cycle count.

[0088] Steps 2-3: Obtain the latest riding coefficient KS_corrected for the initial stage and the riding coefficient KD_corrected for the stable stage, and determine whether the electric vehicle is in the initial stage or the stable stage.

[0089] Steps 2-4: When the use of the electric vehicle is in its initial stage, a riding coefficient model is obtained based on the obtained riding coefficient. The calculation formula is as follows:

[0090]

[0091] In the formula, L1 is the remaining driving range in the initial stage, SOC is the current SOC value of the battery in the electric vehicle, and SOH is the current battery health status in the electric vehicle; the remaining driving range in the initial stage is calculated by obtaining the riding coefficient model of the initial stage.

[0092] When the use of electric vehicles is in a stable phase, the calculation formula for the riding coefficient model based on the obtained riding coefficient is as follows:

[0093]

[0094] In the formula, L2 represents the remaining driving range in the stable phase, SOC represents the current SOC value of the battery in the electric vehicle, and SOH represents the current battery health in the electric vehicle; the remaining driving range in the stable phase is calculated by obtaining the riding coefficient model of the stable phase.

[0095] The physical meaning of the riding coefficient model is as follows: the current remaining battery power (SOC) is first converted into the actual usable battery power under the current temperature and aging conditions, and then multiplied by the ideal distance traveled per unit of battery power to obtain a more accurate estimate of the remaining range. For example, if the current battery power is 55%, the riding coefficient KS_corrected or riding coefficient KD_corrected is 0.5 km / %, the health level is 100, the current temperature compensation coefficient α_current is 0.95, and the current aging compensation coefficient β_current is 0.9, then the remaining driving range L = 55 * 100 * 0.01 * 0.5 * 0.95 * 0.9 ≈ 23.5 kilometers. The calculated remaining driving range result is displayed on the APP interface of the smart terminal, providing users with an intuitive reference. At the same time, the APP in the smart terminal can also convert the mileage into a driving range map or provide a low battery warning as needed; effectively solving the problem of inaccurate display of electric vehicle range and improving the user experience.

[0096] like Figure 3 As shown, an electric vehicle range calculation system based on a smart terminal is provided. This system is used to implement the aforementioned electric vehicle range calculation method based on a smart terminal, and includes:

[0097] Battery Management Module: Used to acquire the state parameters of the battery in the electric vehicle, including battery temperature, battery cycle count, battery SOC value, and battery health status (SOH); it also collects core parameters such as battery voltage and battery current.

[0098] BLE communication module: Used to transmit the acquired battery status parameters in the electric vehicle to the smart terminal processing module;

[0099] Data storage module: Used to store the capacity compensation coefficient library for temperature and the capacity compensation coefficient library for cycle number, and also stores the calculated initial stage riding coefficient KS_corrected and stable stage riding coefficient KD_corrected;

[0100] GPS positioning module: used to obtain the geographical location information of the electric vehicle;

[0101] Intelligent terminal processing module: used to build a riding coefficient model and calculate the remaining driving range based on the riding coefficient model.

[0102] A storage medium containing an executable program, which is executed by a processor to implement the electric vehicle range calculation method based on a smart terminal.

[0103] The BLE communication module, data storage module, GPS positioning module, and smart terminal processing module can be integrated into a smart terminal, which can be a user's smart device such as a mobile phone. Simultaneously, the battery management module in the electric vehicle also includes a battery BLE module. This battery BLE module is connected to the BLE communication module of the smart terminal via a Bluetooth signal connection, used to transmit the battery status parameters collected by the battery management module. The battery BLE module broadcasts or requests the corresponding smart terminal to read the battery status parameters collected by the battery management module at regular intervals, for example, every second.

[0104] The BLE communication module scans and connects to the battery BLE module in the electric vehicle, subscribing to or reading the battery's status parameters. The process of transmitting the battery's status parameters to the intelligent terminal processing module involves the battery management module first collecting the battery's status parameters and transmitting them to the battery BLE module. The battery BLE module then transmits the status parameters to the intelligent terminal's BLE communication module via Bluetooth. The BLE communication module then transmits the status parameters to the intelligent terminal processing module for analysis and processing.

[0105] The GPS positioning module continuously and in real-time acquires the latitude and longitude coordinates of the electric vehicle and calculates the precise actual driving distance D using algorithms. For example, it uses Kalman filtering to filter positioning drift points, calculates the Haversine distance between consecutive points, and accumulates the results to obtain the actual driving distance. The data storage module stores capacity compensation coefficient libraries for temperature and cycle counts, as well as calculated initial stage riding coefficients KS_corrected and stable stage riding coefficients KD_corrected. It also stores battery state parameters acquired during each use of the electric vehicle, calculated actual driving distance, total power consumption, average temperature, and start and end times, etc. The intelligent terminal processing module is used to construct a riding coefficient model based on the state parameters and actual driving distance, and to calculate the remaining driving range of the electric vehicle based on the riding coefficient model.

[0106] All sensitive data processing is completed on the user's local device, including core aspects such as riding trajectory, battery status parameters, and the construction and updating of personal riding habit models. No personal data needs to be uploaded to cloud servers throughout the process, completely eliminating the risk of user privacy data leakage, misuse, or tracking by third parties from a technical architecture perspective. At the same time, it fully utilizes the powerful computing, sensing, and communication capabilities of existing smart terminals, such as smartphones, without requiring large-scale modifications to existing two-wheeled electric vehicle hardware or the use of expensive external equipment; only basic BLE communication functionality of the battery is needed. This significantly lowers the implementation threshold and promotion costs of the solution, facilitating the rapid popularization of the technology.

[0107] Example

[0108] When a user needs to check the remaining driving range of an electric vehicle, the remaining driving range is obtained through a riding coefficient model. Then, the remaining driving range is adjusted based on the different speed modes used during vehicle use and the number of start-stop cycles to arrive at the final remaining driving range. Therefore, a speed mode acquisition device and an electric vehicle start-stop cycle acquisition device are installed. Both devices transmit the collected speed mode and start-stop cycle data to the smart terminal via a battery BLE module. Electric vehicle use includes low-speed, medium-speed, and high-speed modes. Electric vehicles typically have three speed modes, such as economy, comfort, and sport. The number of start-stop cycles is set to at least four start-stop thresholds, first determining the current speed mode of the electric vehicle.

[0109] When the electric vehicle is in low-speed mode, the number of start-stop cycles is counted. If the number of start-stop cycles is below the first start-stop threshold, no adjustment is made to the remaining driving range. If the number of start-stop cycles is higher than or equal to the first start-stop threshold but lower than the second start-stop threshold, the remaining driving range is subtracted by the product of the remaining driving range and the start-stop driving range coefficient δ1 to obtain the final remaining driving range. If the number of start-stop cycles is higher than or equal to the second start-stop threshold but lower than the third start-stop threshold, the remaining driving range is subtracted by the remaining driving range. The remaining driving range is calculated by multiplying the driving range by the start-stop driving range coefficient δ2. If the number of start-stop cycles of the electric vehicle is higher than or equal to the third start-stop threshold but lower than the fourth start-stop threshold, the remaining driving range is subtracted from the product of the remaining driving range multiplied by the start-stop driving range coefficient δ3 to obtain the final remaining driving range. If the number of start-stop cycles of the electric vehicle is higher than or equal to the fourth start-stop threshold, the remaining driving range is subtracted from the product of the remaining driving range multiplied by the start-stop driving range coefficient δ4 to obtain the final remaining driving range.

[0110] When the electric vehicle is in medium-speed mode, the remaining driving range is multiplied by the medium-speed mode coefficient μ1 to obtain the first adjusted remaining driving range. At this point, the number of start-stop cycles is determined. If the number of start-stop cycles is lower than the first start-stop threshold, the first adjusted remaining driving range is used as the final remaining driving range. If the number of start-stop cycles is higher than or equal to the first start-stop threshold but lower than the second start-stop threshold, the first adjusted remaining driving range is subtracted from the product of the first adjusted remaining driving range and the start-stop driving range coefficient δ1 to obtain the final remaining driving range. If the number of start-stop cycles is higher than or equal to the second start-stop threshold but lower than the third start-stop threshold, then... The final remaining driving range is obtained by subtracting the product of the first adjusted remaining driving range and the start-stop driving range coefficient δ2. When the number of start-stop times of the electric vehicle is higher than or equal to the third start-stop threshold but lower than the fourth start-stop threshold, the final remaining driving range is obtained by subtracting the product of the first adjusted remaining driving range and the start-stop driving range coefficient δ3. When the number of start-stop times of the electric vehicle is higher than or equal to the fourth start-stop threshold, the final remaining driving range is obtained by subtracting the product of the first adjusted remaining driving range and the start-stop driving range coefficient δ4.

[0111] When an electric vehicle is in high-speed mode, its power consumption is significantly higher compared to low-speed and medium-speed modes. The number of start-stop cycles has a relatively small impact on power consumption. Therefore, the adjustment of the remaining driving range based on the number of start-stop cycles is corrected. The remaining driving range is multiplied by the high-speed mode coefficient μ2 to obtain the second adjusted remaining driving range. At this point, the number of start-stop cycles of the electric vehicle is determined, and the remaining driving range is adjusted directly based on the number of start-stop cycles. The calculation process is as follows:

[0112]

[0113] In the formula, L ZZ This represents the final remaining driving range; L SY The remaining driving range is μ2×L SY This represents the remaining driving range after the second adjustment; Q is the sum of all start-stop times, δ G This represents the range loss caused by each start-stop cycle of the electric vehicle. The system automatically incorporates user-specific factors and dynamic riding habits when adjusting the remaining range based on different speed modes used and the number of start-stop cycles. This ensures the predicted range closely matches the user's actual usage scenarios, completely resolving the pain point of discrepancies between official ideal operating condition data and real-world experience.

[0114] The above description is merely a preferred embodiment of the present invention. Those skilled in the art can make several modifications and optimizations based on the above disclosure without departing from the basic principles described above. These modifications and optimizations should be considered within the scope of protection as understood by the present invention.

Claims

1. A method for calculating the range of electric vehicles based on intelligent terminals, characterized in that: Includes the following steps: Step 1: The battery management module obtains the status parameters of the battery in the electric vehicle in real time, and the BLE communication module transmits the status parameters to the smart terminal processing module. Establish a capacity compensation coefficient library for temperature and a capacity compensation coefficient library for cycle number based on the battery temperature and battery cycle number parameters in the state parameters; Step 2: Obtain the electric vehicle's geographical location information in real time through the positioning module of the smart terminal, and calculate the actual driving distance of the electric vehicle each time it is used based on the electric vehicle's geographical location information; Step 3: The intelligent terminal processing module correlates the changes in battery state parameters during each use of the electric vehicle with the actual driving distance of the electric vehicle in each use, calculates the temporary unit energy driving coefficient Ki of the electric vehicle in this use, and corrects the temporary unit energy driving coefficient Ki of the electric vehicle in this use by using the corresponding temperature capacity compensation coefficient and cycle number capacity compensation coefficient, to obtain the corrected unit energy driving coefficient of the electric vehicle in this use. And based on the correlation analysis results, a cycling coefficient model is constructed on the smart terminal; The riding coefficient model is constructed into an initial stage and a stable stage; a usage time threshold for electric vehicles is set, with the period from the time the electric vehicle is manufactured to the usage time threshold being defined as the initial stage, and the period after the usage time threshold being defined as the stable stage. In the initial stage, the unit energy travel coefficient of the electric vehicle is obtained for several uses, and the arithmetic mean of the unit energy travel coefficients is calculated. The arithmetic mean of the calculated unit energy travel coefficients is used as the riding coefficient. Furthermore, the cycling coefficient is calculated at intervals T1. Perform real-time updates; During the stable phase, the unit energy travel coefficient of the electric vehicle was obtained several times. A weighted average method was then used to assign weights to each unit energy travel coefficient in chronological order, from oldest to most recent, to obtain the riding coefficient. Furthermore, the cycling coefficient is calculated at intervals T2. Perform real-time updates; Step 4: Calculate and output the remaining driving range of the electric vehicle based on the current state parameters of the battery and the established riding coefficient model.

2. The method for calculating the range of an electric vehicle based on a smart terminal according to claim 1, characterized in that: The state parameters of the battery in the electric vehicle include battery temperature, battery cycle count, battery SOC value, and battery health status (SOH). The temperature capacity compensation coefficient library contains several temperature capacity compensation coefficients, which are calculated using the battery's standard capacity and the measured capacity of the battery at different temperatures. The calculation process is as follows: In the formula, α is the capacity compensation coefficient for temperature. For standard battery capacity, This represents the measured capacity of the battery at a certain temperature.

3. The method for calculating the range of an electric vehicle based on a smart terminal according to claim 1, characterized in that: The capacity compensation coefficient library for the number of cycles contains capacity compensation coefficients for several number of cycles. These capacity compensation coefficients for several number of cycles are calculated based on the standard battery capacity and the measured capacity at different number of cycles. The calculation process is as follows: In the formula, β is the capacity compensation coefficient for the number of iterations. For standard battery capacity, This represents the measured capacity of the battery after a certain number of cycles.

4. The method for calculating the range of an electric vehicle based on a smart terminal according to claim 1, characterized in that: In step three, the intelligent terminal processing module performs a correlation analysis between the changes in the battery's state parameters during each use of the electric vehicle and the actual driving distance of the electric vehicle in each use. The correlation analysis between the changes in the battery's state parameters during a particular use of the electric vehicle and the actual driving distance includes the following steps: Step 1-1: Obtain the start and end SOC values ​​of the electric vehicle for this usage, and calculate the total power consumption. ; Step 1-2: Obtain the real-time geographical location information of the electric vehicle during this use, and calculate the actual driving distance D of the electric vehicle during this use; Steps 1-3: Calculate the temporary unit electricity driving coefficient Ki of the electric vehicle using the formula. The calculation process is as follows: In the formula, Ki is the temporary unit energy travel coefficient used by the electric vehicle this time, which is the temporary unit energy travel coefficient used by the electric vehicle for the i-th time; D is the actual travel distance of the electric vehicle this time. This represents the total power consumption of the electric vehicle during this use; SOH i The health status of the battery during the i-th use of the electric vehicle; Steps 1-4: Correct the temporary unit energy driving coefficient Ki of the electric vehicle in this operation using the corresponding temperature capacity compensation coefficient and cycle number capacity compensation coefficient to obtain the corrected unit energy driving coefficient Ki_corrected. The calculation process is as follows: In the formula, Ki_corrected is the corrected unit power driving coefficient of the electric vehicle in this cycle, α is the capacity compensation coefficient for temperature, and β is the capacity compensation coefficient for the number of cycles.

5. The method for calculating the range of an electric vehicle based on a smart terminal according to claim 4, characterized in that: The cycling coefficient model, KS_corrected, is constructed on the smart terminal based on the correlation analysis results. The calculation process is as follows: In the formula, Ki_corrected is the corrected unit energy travel coefficient of the electric vehicle used for the i-th time, and i is the number of unit energy travel coefficients used by the electric vehicle.

6. The method for calculating the range of an electric vehicle based on a smart terminal according to claim 5, characterized in that: Cycling coefficient KD_corrected; The calculation process is as follows: In the formula, Wi is the weight of the unit energy driving coefficient used by the electric vehicle in the i-th time, and i is the number of unit energy driving coefficients used by the electric vehicle.

7. The method for calculating the range of an electric vehicle based on a smart terminal according to claim 6, characterized in that: When a user needs to check the current remaining driving range of an electric vehicle, the following steps are involved: Step 2-1: Obtain the current battery status parameters in the electric vehicle in real time through the BLE module; Step 2-2: Based on the battery temperature and battery cycle count in the current battery state parameters of the electric vehicle, query the corresponding capacity compensation coefficient for temperature in the capacity compensation coefficient library and the capacity compensation coefficient library for cycle count. Capacity compensation coefficient for the number of cycles ; Steps 2-3: Obtain the latest initial stage riding coefficients. and the cycling coefficient during the stable phase And determine whether the use of electric vehicles is in the initial stage or a stable stage; Steps 2-4: When the use of the electric vehicle is in its initial stage, a riding coefficient model is obtained based on the obtained riding coefficient. The calculation formula is as follows: In the formula, L1 is the remaining driving range in the initial stage, SOC is the current SOC value of the battery in the electric vehicle, and SOH is the current battery health status in the electric vehicle. When the use of electric vehicles is in a stable phase, the calculation formula for the riding coefficient model based on the obtained riding coefficient is as follows: In the formula, L2 is the remaining driving range in the stable phase, SOC is the current SOC value of the battery in the electric vehicle, and SOH is the current battery health status of the electric vehicle.

8. A smart terminal-based electric vehicle range calculation system, the system being used to implement the smart terminal-based electric vehicle range calculation method according to any one of claims 1-7, comprising: Battery management module: used to acquire the state parameters of the battery in the electric vehicle, including battery temperature, battery cycle count, battery SOC value, and battery health status (SOH). BLE communication module: Used to transmit the acquired battery status parameters in the electric vehicle to the smart terminal processing module; Data storage module: Used to store the capacity compensation coefficient library for temperature and the capacity compensation coefficient library for the number of cycles, as well as the calculated initial stage riding coefficient. and stable phase riding coefficient ; GPS positioning module: used to obtain the geographical location information of the electric vehicle; Intelligent terminal processing module: used to build a riding coefficient model and calculate the remaining driving range based on the riding coefficient model.

9. A storage medium, characterized in that: It contains an executable program, which, when executed by a processor, can implement the electric vehicle range calculation method based on a smart terminal as described in any one of claims 1-7.

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