An electric vehicle range prediction and battery capacity management method and system

Abstract

The application relates to the technical field of vehicles and discloses a method, a system, an electronic device, a storage medium and a vehicle for predicting the driving range of an electric vehicle and managing the capacity of a battery, which comprises the following steps: in response to real-time collected vehicle operation data, the operation data is pretreated; in response to the pretreated operation data, an initial prediction model of the driving range of the vehicle is constructed; the output of the initial prediction model is dynamically adjusted based on the proportional link in a PID algorithm, so that the prediction accuracy of the initial prediction model is improved; the capacity of the battery is adjusted based on the vehicle use data and the battery state data, including battery health state monitoring, battery capacity calibration, charging strategy optimization and battery use mode adjustment; and the current driving range of the vehicle is calculated in combination with the optimized initial prediction model and the adjusted battery capacity information and is output to a driver. The application can effectively improve the prediction accuracy of the driving range of the electric vehicle and the use efficiency of the battery.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method, system, electronic device, storage medium, and vehicle for predicting the driving range and managing the battery capacity of an electric vehicle. Background Technology

[0002] Currently, in the field of electric vehicles, the accurate calculation of driving range remains a hot research topic and a challenge. Existing technologies typically employ predictive models based on statistical methods or machine learning algorithms to calculate the driving range of electric vehicles. These models primarily rely on the operating data of the electric vehicle, such as speed, acceleration, and battery voltage, and predict future driving range by analyzing and processing this data.

[0003] Furthermore, existing technologies primarily manage battery capacity through monitoring and adjustment via a Battery Management System (BMS). A BMS can monitor the battery's status and parameters in real time, including voltage, current, temperature, and SOC (State of Charge). Based on this information, the BMS can control the battery's charging and discharging to ensure battery safety and performance.

[0004] The shortcomings of existing technologies mainly include the following aspects:

[0005] Insufficient accuracy in range prediction: Existing range prediction models are often affected by various factors, such as road conditions, driving habits, and ambient temperature. These factors can lead to significant discrepancies between the predicted results and the actual values, thus impacting the driver's travel planning.

[0006] Insufficient precision in battery capacity management: While existing battery management systems (BMS) can perform basic monitoring and adjustments, they still fall short in optimizing and adjusting battery capacity. Battery capacity degradation and changes are influenced by various factors, such as the number of charge-discharge cycles and operating temperature. Existing technologies often lack comprehensive consideration of these factors, resulting in inaccurate battery capacity management.

[0007] Lack of real-time optimization and adjustment mechanisms: Existing predictive models and battery management systems often lack real-time optimization and adjustment mechanisms. In actual use, the operating state of electric vehicles and the state of the battery may change, and existing technologies often cannot respond to and adjust to these changes in a timely manner, thus affecting prediction accuracy and battery efficiency.

[0008] Therefore, this application provides a method for predicting the driving range of electric vehicles and managing battery capacity to solve the above-mentioned technical problems. Summary of the Invention

[0009] The purpose of this invention is to provide a method, system, electronic device, storage medium, and vehicle for predicting the driving range and managing the battery capacity of electric vehicles, so as to solve the technical problems of low accuracy in driving range prediction and low battery utilization efficiency in the prior art.

[0010] To address the aforementioned technical problems, this invention provides a method for predicting the driving range of an electric vehicle and managing its battery capacity, comprising:

[0011] The data collection and preprocessing steps include preprocessing the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data;

[0012] The step of constructing an initial prediction model includes constructing an initial prediction model of the vehicle's driving range in response to the preprocessed operating data, wherein the initial prediction model is used to predict the vehicle's driving range.

[0013] The initial prediction model optimization steps include dynamically adjusting the output of the initial prediction model based on the proportional element in the PID algorithm to improve the prediction accuracy of the initial prediction model.

[0014] The battery capacity optimization and adjustment steps include adjusting the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization, and battery usage mode adjustment.

[0015] The driving range calculation and output steps include combining the optimized initial prediction model and the adjusted battery capacity information to calculate the vehicle's current driving range and output it to the driver.

[0016] In some specific embodiments, the data collection and preprocessing step includes preprocessing the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data, and further includes:

[0017] The vehicle usage data includes driving speed, acceleration, gradient, and mileage;

[0018] The battery status data includes voltage, current, temperature, and internal resistance;

[0019] The collected vehicle usage data and battery status data are cleaned and normalized to ensure data integrity and accuracy.

[0020] In some specific embodiments, the step of constructing an initial prediction model includes, in response to the preprocessed operational data, constructing the initial prediction model for the vehicle's driving range, wherein the initial prediction model is used to predict the vehicle's driving range, and further includes:

[0021] The preprocessed running data is divided into a training set and a test set for model training and validation.

[0022] Based on the predicted demand and the characteristics of the operational data, select the appropriate machine learning algorithm;

[0023] The machine learning algorithm is trained using the data in the training set to construct the initial prediction model, wherein the initial prediction model is able to predict the vehicle's range based on the input operating data.

[0024] The initial prediction model is evaluated using the data from the test set to verify its prediction accuracy and generalization ability.

[0025] In some specific embodiments, the initial prediction model optimization step, including dynamically adjusting the output of the initial prediction model based on the proportional element in the PID algorithm to improve the prediction accuracy of the initial prediction model, further includes:

[0026] The proportional coefficient of the proportional element is dynamically adjusted based on the error between the output of the initial prediction model calculated in real time and the actual driving situation.

[0027] The adjusted proportional coefficient is applied to the calculation formula of the proportional element to generate a new control quantity;

[0028] The new control variable is applied to the initial prediction model, and the model output is adjusted in real time to improve prediction accuracy.

[0029] In some specific embodiments, the battery capacity optimization and adjustment steps include adjusting the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization, and battery usage mode adjustment, and further include:

[0030] Monitor key indicators in the battery status data and assess battery health.

[0031] The battery capacity is calibrated by calculating the actual battery capacity based on the changes in charge recorded during the full charge and discharge process.

[0032] Based on the current battery status data and usage requirements, optimize the charging strategy and adjust the charging parameters;

[0033] Based on the vehicle usage data and battery characteristics, the vehicle battery usage mode is adjusted to extend battery life.

[0034] In some specific embodiments, the driving range calculation and output step, which includes combining the optimized initial prediction model and adjusted battery capacity information to calculate the vehicle's current driving range and output it to the driver, further includes:

[0035] The current driving range of the vehicle is calculated by combining the optimized initial prediction model and the adjusted battery capacity information.

[0036] The calculated driving range information is presented to the driver in a visual or interactive way.

[0037] Based on the same concept, the present invention also provides an electric vehicle range prediction and battery capacity management system, comprising:

[0038] The data collection and preprocessing module is configured to preprocess the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data.

[0039] An initial prediction model module is configured to construct an initial prediction model for the vehicle's driving range in response to the preprocessed running data, wherein the initial prediction model is used to predict the vehicle's driving range.

[0040] The initial prediction model optimization module is configured to dynamically adjust the output of the initial prediction model based on the proportional element in the PID algorithm, so as to improve the prediction accuracy of the initial prediction model.

[0041] The battery capacity optimization and adjustment module is configured to adjust the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization, and battery usage mode adjustment.

[0042] The driving range calculation and output module is configured to combine the optimized initial prediction model and the adjusted battery capacity information to calculate the vehicle's current driving range and output it to the driver.

[0043] Based on the same concept, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of an electric vehicle range prediction and battery capacity management method.

[0044] Based on the same concept, the present invention also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of an electric vehicle range prediction and battery capacity management method.

[0045] Based on the same concept, the present invention also provides a vehicle equipped with an electric vehicle range prediction and battery capacity management system as described above.

[0046] Compared with existing technologies, its advantages are as follows:

[0047] This invention discloses a method, system, electronic device, storage medium, and vehicle for predicting the driving range and managing the battery capacity of electric vehicles, which can effectively improve the accuracy of driving range prediction and battery utilization efficiency of electric vehicles. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating some specific embodiments of the electric vehicle range prediction and battery capacity management method of the present invention;

[0049] Figure 2 This is a schematic diagram of the PID algorithm in some applications of the electric vehicle range prediction and battery capacity management method of the present invention;

[0050] Figure 3 This is a schematic diagram of the structure of an electric vehicle range prediction and battery capacity management system in some specific embodiments of the present invention;

[0051] Figure 4 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0054] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0055] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0056] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0057] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0058] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0059] Reference Figure 1 A method for predicting the driving range and managing the battery capacity of an electric vehicle, comprising:

[0060] S101, a data collection and preprocessing step, including preprocessing the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data;

[0061] In some of these applications, the data collection and preprocessing steps include preprocessing the operational data in response to real-time collected vehicle operational data, which includes vehicle usage data and battery status data. Vehicle usage data includes driving speed, acceleration, gradient, and mileage; battery status data includes voltage, current, temperature, and internal resistance. The collected vehicle usage data and battery status data are then cleaned and normalized to ensure data integrity and accuracy.

[0062] Understandably, real-time operational data is captured from the electric vehicle's sensor network (such as speed sensors, acceleration sensors, gradient sensors, odometers, etc.) and battery management system (BMS). Connections to the sensors and BMS are made via standard CAN bus, LIN bus, or dedicated interfaces to ensure data real-time performance and accuracy. Real-time data collection includes vehicle speed (e.g., current speed 50 km / h) and acceleration (e.g., rate of change of acceleration 0.5 m / s²). 2 The system collects data on the following parameters: slope (e.g., current driving slope 5%), mileage (e.g., cumulative mileage 1000km), battery voltage (e.g., current voltage 330V), current (e.g., discharge current 100A), temperature (e.g., battery temperature 25℃), and internal resistance (e.g., current internal resistance 50mΩ). Reasonable thresholds are set (e.g., speed not exceeding the legal speed limit of 120km / h, acceleration change rate not exceeding ±3m / s²). 2 The system identifies and removes outliers (provided the temperature does not exceed the normal operating range of the battery, such as -20℃ to 60℃). For example, if a speed data of 200km / h is detected, it is judged as an anomaly and removed. For missing values ​​caused by sensor malfunction or data transmission problems, forward padding, backward padding, or interpolation methods (such as linear interpolation, polynomial interpolation, etc.) are used to complete the missing values.

[0063] To eliminate the influence of different units and ranges on data processing and improve the model's convergence speed and prediction accuracy, a min-max scaling method is used to transform the data to the range of 0 to 1. The specific formula is as follows:

[0064]

[0065] Where x is the original data, x min and x max These are the minimum and maximum values ​​in the dataset, x. norm It is the normalized data.

[0066] For example, for battery voltage data, assuming its minimum value is 200V, its maximum value is 400V, and the current voltage is 330V, then the normalized voltage is:

[0067]

[0068] Data that has been cleaned and normalized can be stored in a database.

[0069] S102, the step of constructing an initial prediction model includes constructing an initial prediction model of the vehicle's driving range in response to the preprocessed operating data, wherein the initial prediction model is used to predict the vehicle's driving range.

[0070] In some applications, the step of building an initial prediction model includes: in response to preprocessed operational data, building an initial prediction model for the vehicle's driving range; dividing the preprocessed operational data into training and test sets for model training and validation; selecting an appropriate machine learning algorithm based on the prediction requirements and the characteristics of the operational data; training the machine learning algorithm using the training set data to build the initial prediction model, which can predict the vehicle's driving range based on the input operational data; and evaluating the initial prediction model using the test set data to validate its prediction accuracy and generalization ability.

[0071] Understandably, the preprocessed operational data (including vehicle usage data and battery status data) is randomly divided into two parts according to a certain ratio (e.g., 70% training set, 30% test set). Based on the prediction requirement (i.e., predicting the vehicle's driving range) and the characteristics of the operational data (e.g., time series nature, non-linear relationships, etc.), a machine learning algorithm is selected. Common algorithms include linear regression, decision trees, random forests, gradient boosting trees (GBDT), and neural networks.

[0072] For example, considering that vehicle range prediction may involve complex relationships between multiple variables, gradient boosting tree (GBDT) is chosen as the initial algorithm because it is good at handling nonlinear relationships and capturing the interaction effects between variables.

[0073] The selected machine learning algorithm is trained using the training set data. During training, the algorithm learns from the input runtime data (such as driving speed, acceleration, gradient, mileage, battery voltage, current, temperature, internal resistance, etc.) and the corresponding range labels, establishing a mapping relationship between input and output.

[0074] For example, in GBDT model training, multiple decision trees are built through continuous iteration. Each tree attempts to correct the prediction error of the previous tree, eventually forming a strong learner to predict the vehicle's driving range.

[0075] The trained initial prediction model is evaluated using data from the test set to verify its prediction accuracy and generalization ability.

[0076] For example, the root mean square error (RMSE) can be chosen as the evaluation metric to calculate the RMSE value between the actual driving range on the test set and the model's predicted driving range. A smaller RMSE value indicates higher model prediction accuracy; if the RMSE value on the test set is similar to or slightly higher than that on the training set, it indicates better model generalization ability.

[0077] Based on the evaluation results, the model is adjusted and optimized. This includes adjusting algorithm parameters, changing the algorithm, and adding feature engineering (such as feature selection and feature transformation) to improve the model's prediction accuracy and generalization ability.

[0078] For example, if the initial GBDT model has a high RMSE value, you can try adjusting parameters such as tree depth and learning rate, or introducing more relevant features (such as weather conditions, driver habits, etc.) to achieve better prediction results.

[0079] S103, Initial prediction model optimization step, including dynamically adjusting the output of the initial prediction model based on the proportional element in the PID algorithm to improve the prediction accuracy of the initial prediction model;

[0080] In some applications, the initial prediction model optimization step includes dynamically adjusting the output of the initial prediction model based on the proportional element in the PID algorithm to improve the prediction accuracy of the initial prediction model. This involves dynamically adjusting the proportional coefficient of the proportional element based on the error between the output of the initial prediction model calculated in real time and the actual driving conditions; applying the adjusted proportional coefficient to the calculation formula of the proportional element to generate a new control quantity; and applying the new control quantity to the initial prediction model to adjust the model output in real time to improve prediction accuracy.

[0081] Understandably, the proportional element in the PID control algorithm is initialized by setting an initial proportional coefficient Kp to initially adjust the deviation between the model output and the actual value. Real-time vehicle driving data is collected, and the output of the initial prediction model is compared with the actual driving data to calculate the error E(t). The error E(t) can be defined as:

[0082] E(t)=Yactual(t)-Ypredicted(t)

[0083] Where Yactual(t) represents the actual driving data at time t, and Ypredicted(t) represents the model's predicted output at time t.

[0084] Based on the magnitude and direction of the error E(t), analyze the nature of the error (such as positive deviation, negative deviation, and magnitude of deviation).

[0085] Based on the error analysis results, the proportional coefficient Kp is dynamically adjusted. When the error is large, the value of Kp is increased to speed up the adjustment; when the error is small, the value of Kp is decreased to avoid over-adjustment. The specific adjustment formula could be:

[0086] Kp new =Kp old +α·sign(E(t))·|E(t)| β

[0087] Where α is the learning rate, which controls the adjustment magnitude; β is the adjustment factor, which affects the sensitivity of the error magnitude to the adjustment of Kp; and sign(E(t)) is the sign function, which is used to determine the adjustment direction.

[0088] The adjusted proportional coefficient Kp new In the calculation formula applied to the proportional element, the control quantity U(t) is recalculated:

[0089] U(t)=Kp new ·E(t)

[0090] U(t) will be used as a new control variable to adjust the output of the initial prediction model.

[0091] By applying the new control variable U(t) to the initial prediction model and modifying the model's internal parameters or calculation process, the model's output can be adjusted in real time, reducing the error between the model output and the actual driving data, thereby improving the prediction accuracy.

[0092] The scaling factor is continuously adjusted dynamically based on real-time data to optimize the output of the prediction model. Through multiple iterations, the optimal scaling factor can be gradually approximated, resulting in a significant improvement in the accuracy of the prediction model.

[0093] S104, Battery capacity optimization and adjustment steps, including adjusting the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization and battery usage mode adjustment;

[0094] In some applications, battery capacity optimization and adjustment steps include adjusting battery capacity based on vehicle usage data and battery status data. This includes monitoring key indicators in battery status data and assessing battery health status, as well as optimizing charging strategies and adjusting battery usage modes. Battery capacity is calibrated by calculating the actual battery capacity based on the charge changes recorded during a full charge and discharge process. Charging strategies are optimized and charging parameters are adjusted based on current battery status data and usage requirements. Finally, vehicle battery usage modes are adjusted based on vehicle usage data and battery characteristics to extend battery life.

[0095] It is understandable that key indicators in battery status data should be continuously monitored, including but not limited to battery voltage, current, temperature, internal resistance, and number of charge-discharge cycles.

[0096] For example, monitoring showed that after one charge-discharge cycle, the battery's internal resistance increased by 5 mΩ compared to the previous cycle, and the peak temperature exceeded the recommended 45°C. This indicates a decline in battery health.

[0097] The actual battery capacity is calculated based on the changes in charge recorded during a full charge and discharge process, and then compared with the nominal capacity to calibrate the battery capacity.

[0098] Based on current battery status data (such as health status, remaining charge, temperature, etc.) and user needs (such as expected driving range, charging time preferences, etc.), optimize the charging strategy and adjust charging parameters such as charging current, voltage, and time.

[0099] For example, if the battery temperature is detected to be 25°C, the remaining charge is 20%, and the user plans to fully charge it within 2 hours for a long trip, the charging strategy will be automatically adjusted to a fast charging mode, charging with higher current and voltage while ensuring the battery temperature remains within a safe range.

[0100] Based on vehicle usage data (such as driving speed, acceleration and deceleration frequency, road condition information, etc.) and battery characteristics (such as energy density, charge and discharge efficiency, etc.), the vehicle battery usage mode is adjusted, such as energy recovery intensity and power output strategy, in order to reduce unnecessary battery wear and extend battery life.

[0101] For example, when driving on congested city roads, given the frequent starts and stops and low-speed driving characteristics, the intervention intensity of the energy recovery system is increased to convert braking energy into electrical energy for storage. At the same time, the power output strategy is adjusted to reduce the number of rapid accelerations and decelerations, thereby reducing the number of high-current discharges and recharges of the battery, thus protecting the battery and extending its lifespan.

[0102] S105, the driving range calculation and output step, includes combining the optimized initial prediction model and the adjusted battery capacity information to calculate the vehicle's current driving range and output it to the driver.

[0103] In some applications, the driving range calculation and output steps include combining the optimized initial prediction model and the adjusted battery capacity information to calculate the vehicle's current driving range and output it to the driver; combining the optimized initial prediction model and the adjusted battery capacity information to calculate the vehicle's current driving range; and presenting the calculated driving range information to the driver in a visual or interactive manner.

[0104] Understandably, by combining the optimized initial prediction model with the adjusted battery capacity information, the remaining driving range of the vehicle in the current state can be calculated based on the current driving status of the vehicle (such as speed, acceleration, load, etc.) and external environmental conditions.

[0105] For example, if the vehicle is currently traveling at a constant speed of 60 km / h on a flat road, the outside temperature is 20°C, and the battery SOC is 70% (i.e., the remaining charge is 58 kWh * 70% = 40.6 kWh), the optimized prediction model, based on these parameters and considering factors such as the vehicle's powertrain efficiency and drag coefficient, calculates that the vehicle's remaining range under these conditions is approximately 350 km.

[0106] The calculated driving range information is presented to the driver in a visual or interactive way. This can be achieved through in-vehicle displays, such as digital displays on the instrument panel, graphical interfaces, or voice prompts.

[0107] For example, on the in-vehicle display, the remaining driving range information is shown digitally on the main interface, along with graphical representations (such as a battery level bar or a driving range graph). Drivers can also interact with the system via touchscreen or voice commands to access more detailed driving range information or adjust relevant settings. Furthermore, alerts are issued based on remaining battery power and estimated driving range, reminding drivers to charge the battery or adjust their travel plans accordingly.

[0108] The following is combined with Figure 2 This invention illustrates embodiments of the electric vehicle range prediction and battery capacity management method in some applications:

[0109] In PID algorithms, the proportional gain (P-terminal) directly generates the control input based on the error signal. This embodiment dynamically adjusts the proportional gain of the P-terminal by collecting real-time vehicle usage data and considering the current battery state, adapting to different driving conditions and battery status, thereby improving the accuracy of range calculation. The calculation formula is as follows:

[0110] The formula for calculating the P-terminal is: P = K_p * e(t), where:

[0111] (P) is the output of the P stage.

[0112] (K_p) is the proportional coefficient, which is an adjustable parameter that needs to be adjusted according to the actual situation.

[0113] (e(t)) is the error at the current time, which is the difference between the expected output and the actual output.

[0114] In the calculation of the driving range of electric vehicles, (e(t)) can be the difference between the expected driving range and the actual driving range.

[0115] Battery capacity optimization and adjustment:

[0116] The battery capacity (C) is calculated based on the battery's nominal capacity and current state. In this embodiment, it is adjusted according to the battery's real-time state and usage data. Simplified here, it is described as: C_actual = C_rated * f(S, H, T, ...)

[0117] in:

[0118] C_actual is the actual capacity of the battery.

[0119] C_rated is the nominal capacity of the battery.

[0120] f(S, H, T, ...) is an adjustment function that adjusts the battery capacity based on factors such as the battery's state ((S)), historical data ((H)), and temperature ((T)).

[0121] Battery health status monitoring: Real-time monitoring of key battery indicators, such as internal resistance and capacity decay rate, through BMS to assess the battery's health status.

[0122] Battery capacity calibration: Perform battery capacity calibration regularly by recording changes in charge level during a full charge and discharge process to obtain accurate battery capacity data.

[0123] Charging strategy optimization: Optimize the charging strategy based on the current battery status and usage requirements, such as adopting fast charging or slow charging modes, and adjusting the charging current and voltage.

[0124] Battery usage mode adjustment: Adjust the usage mode of electric vehicles according to battery type and characteristics, such as avoiding use in extreme temperature environments, and optimizing driving speed and acceleration / deceleration frequency.

[0125] Driving range calculation and output: Combining the optimized prediction model and battery capacity information, the current driving range of the electric vehicle is calculated and output to the driver or relevant systems via the in-vehicle display or mobile app. The specific calculation is as follows:

[0126] The formula for calculating driving range (R) can be based on the current battery level Q_current and the actual battery capacity C_actual: R = (Q_current / E_avg) * C_actual

[0127] in:

[0128] (R) is the projected driving range.

[0129] (C_actual) is the current battery level.

[0130] (E_avg) is the average energy consumption of an electric vehicle, estimated based on historical driving data and current driving conditions;

[0131] In practical applications, PID algorithms and battery capacity optimization complement each other. PID algorithms reduce errors (i.e., the prediction error of driving range) by adjusting the output, while battery capacity optimization provides more accurate battery capacity information, making the prediction of driving range more accurate.

[0132] The following describes this embodiment in conjunction with an application scenario:

[0133] The sensors used in this embodiment include speed sensors, current sensors, temperature sensors, etc., which can meet the needs of real-time data collection for electric vehicles.

[0134] Battery Management Unit (BMU):

[0135] The Battery Management Unit (BMU) is one of the key components in electric vehicles, responsible for real-time monitoring and management of battery status. The BMU used in this embodiment features high precision, high reliability, and fast response, enabling it to accurately predict and correct for battery capacity degradation.

[0136] Energy consumption model:

[0137] Energy consumption models are one of the key factors in predicting the driving range of electric vehicles. The energy consumption model used in this embodiment is based on a large amount of experimental data and actual driving experience, and can accurately estimate the energy consumption of electric vehicles under different environmental conditions.

[0138] PID algorithm:

[0139] In this embodiment, the PID algorithm is used to optimize the accuracy of range prediction by continuously adjusting the PID parameters to reduce errors and find the optimal parameter combination.

[0140] like Figure 2 As shown:

[0141] The PID algorithm adjusts the controller's output through three parts: proportional, integral, and derivative, so that the trolley can calculate the accurate driving range under different conditions.

[0142] Proportional section: The proportional controller generates a control quantity based on the current error.

[0143] Error definition: Set the expected driving range as P target The actual driving range is P. actual Then the error e(t) is defined as:

[0144] e(t) = P target -P actual

[0145] Proportional term calculation: proportional gain K p Multiply by the current error to generate the proportional control value.

[0146] Proportionalterm = K p ·e(t)

[0147] The proportional term enables the system to respond quickly. When the expected deviation from the target value is large, it outputs a larger control value to quickly adjust the driving range calculation.

[0148] Integral section: The integral controller accumulates historical errors to eliminate static errors in the system, ensuring that the system can stably operate near the actual driving range based on the calculated results.

[0149] The integral term accumulates historical errors to counteract situations where the system deviates from the actual driving range over a long period.

[0150]

[0151] Among them, K i This is the integral gain.

[0152] The derivative component: The derivative controller predicts future trends based on the rate of change of error and adjusts the control output accordingly to improve the stability and response speed of the system.

[0153] The differential term predicts future errors by measuring the rate of change of the driving range error.

[0154]

[0155] The outputs of the three parts are weighted and summed to obtain the final PID control output, which is used to adjust the driving range output of the electric vehicle, thereby calculating the accurate driving range.

[0156]

[0157] The following real-time data is acquired through onboard sensors: Current battery capacity C current Current ambient temperature T, power consumption P over the last 25 km avg25 Current wind speed V wind Current altitude A, current vehicle speed V car The above parameters are input into the adjustment function f(C) obtained by training the test data of this model of vehicle. current T, P avg25 V wind V car Output the calculated driving range P. targetThis driving range is then fed into the PID controller, and the initial PID parameters are set as follows: proportional gain K. p =0.5; Integral gain K i =0.02; Differential gain K d =0.1; During the control process, in the experimental stage, the initial parameters are continuously adjusted according to the output results, and finally the gain parameters after the stable output are obtained.

[0158] For the purpose of simplicity, the method steps disclosed in the above embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0159] like Figure 3 As shown, the present invention also provides an electric vehicle range prediction and battery capacity management system, comprising:

[0160] The data collection and preprocessing module 201 is configured to preprocess the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data.

[0161] The initial prediction model construction module 202 is configured to construct the initial prediction model of the vehicle's driving range in response to the preprocessed running data, wherein the initial prediction model is used to predict the vehicle's driving range.

[0162] The initial prediction model optimization module 203 is configured to dynamically adjust the output of the initial prediction model based on the proportional element in the PID algorithm, so as to improve the prediction accuracy of the initial prediction model.

[0163] The battery capacity optimization and adjustment module 204 is configured to adjust the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization, and battery usage mode adjustment.

[0164] The driving range calculation and output module 205 is configured to combine the optimized initial prediction model and the adjusted battery capacity information to calculate the current driving range of the vehicle and output it to the driver.

[0165] It is worth noting that although only some basic functional modules are disclosed in the embodiments of this invention, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be considered as the scope of protection of the claims of this invention being limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0166] like Figure 4 As shown, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of an electric vehicle range prediction and battery capacity management method.

[0167] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 4 The structure shown in this embodiment of the invention includes an electronic device comprising one or more processors 710 and a storage device 720; the processors 710 in this electronic device may be one or more. Figure 4 Taking a processor 710 as an example; a storage device 720 is used to store one or more programs; the one or more programs are executed by the one or more processors 710, so that the one or more processors 710 implement the electric vehicle range prediction and battery capacity management method as described in any one of the embodiments of the present invention.

[0168] The electronic device may also include an input device 730 and an output device 740.

[0169] The processor 710, storage device 720, input device 730, and output device 740 in this electronic device can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0170] The storage device 720 in this electronic device serves as a computer-readable storage medium, capable of storing one or more programs. These programs can be software programs, computer-executable programs, or modules, such as the program instructions / modules corresponding to the electric vehicle range prediction and battery capacity management method provided in this embodiment of the invention. The processor 710 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the storage device 720, thereby implementing the electric vehicle range prediction and battery capacity management method described in the above method embodiment.

[0171] Storage device 720 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, storage device 720 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, storage device 720 may further include memory remotely located relative to processor 710, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0172] Input device 730 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the electronic device. Output device 740 may include display devices such as a display screen.

[0173] The present invention also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of an electric vehicle range prediction and battery capacity management method.

[0174] Specifically, the computer storage medium in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0175] The present invention also provides a vehicle equipped with an electric vehicle range prediction and battery capacity management system as described above.

[0176] 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; and these 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.

Claims

1. A method for predicting the driving range and managing the battery capacity of an electric vehicle, characterized in that, include: The data collection and preprocessing steps include preprocessing the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data; The step of constructing an initial prediction model includes constructing an initial prediction model of the vehicle's driving range in response to the preprocessed operating data, wherein the initial prediction model is used to predict the vehicle's driving range. The initial prediction model optimization steps include dynamically adjusting the output of the initial prediction model based on the proportional element in the PID algorithm to improve the prediction accuracy of the initial prediction model. The battery capacity optimization and adjustment steps include adjusting the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization, and battery usage mode adjustment. The driving range calculation and output steps include combining the optimized initial prediction model and the adjusted battery capacity information to calculate the vehicle's current driving range and output it to the driver. The initial prediction model optimization steps include dynamically adjusting the output of the initial prediction model based on the proportional element in the PID algorithm to improve the prediction accuracy of the initial prediction model, and further include: The proportional coefficient of the proportional element is dynamically adjusted based on the error between the output of the initial prediction model calculated in real time and the actual driving situation. The adjusted proportional coefficient is applied to the calculation formula of the proportional element to generate a new control quantity; The new control variable is applied to the initial prediction model, and the model output is adjusted in real time to improve prediction accuracy.

2. The method for predicting the driving range and managing the battery capacity of an electric vehicle according to claim 1, characterized in that, The data collection and preprocessing steps include preprocessing the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data, and further include: The vehicle usage data includes driving speed, acceleration, gradient, and mileage; The battery status data includes voltage, current, temperature, and internal resistance; The collected vehicle usage data and battery status data are cleaned and normalized to ensure data integrity and accuracy.

3. The method for predicting the driving range and managing the battery capacity of an electric vehicle according to claim 1, characterized in that, The step of constructing an initial prediction model includes, in response to the preprocessed operational data, constructing the initial prediction model for the vehicle's driving range, wherein the initial prediction model is used to predict the vehicle's driving range, and further includes: The preprocessed running data is divided into a training set and a test set for model training and validation. Based on the predicted demand and the characteristics of the operational data, select the appropriate machine learning algorithm; The machine learning algorithm is trained using the data in the training set to construct the initial prediction model, wherein the initial prediction model is able to predict the vehicle's range based on the input operating data. The initial prediction model is evaluated using the data from the test set to verify its prediction accuracy and generalization ability.

4. The method for predicting the driving range and managing the battery capacity of an electric vehicle according to claim 1, characterized in that, The battery capacity optimization and adjustment steps include adjusting the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization, and battery usage mode adjustment, and further include: Monitor key indicators in the battery status data and assess battery health. The battery capacity is calibrated by calculating the actual battery capacity based on the changes in charge recorded during the full charge and discharge process. Based on the current battery status data and usage requirements, optimize the charging strategy and adjust the charging parameters; Based on the vehicle usage data and battery characteristics, the vehicle battery usage mode is adjusted to extend battery life.

5. The method for predicting the driving range and managing the battery capacity of an electric vehicle according to claim 1, characterized in that, The driving range calculation and output steps, including combining the optimized initial prediction model and adjusted battery capacity information to calculate the vehicle's current driving range and output it to the driver, further include: The current driving range of the vehicle is calculated by combining the optimized initial prediction model and the adjusted battery capacity information. The calculated driving range information is presented to the driver in a visual or interactive way.

6. An electric vehicle range prediction and battery capacity management system, characterized in that, include: The data collection and preprocessing module is configured to preprocess the vehicle operation data in response to real-time collected vehicle operation data, wherein the operation data includes vehicle usage data and battery status data. An initial prediction model module is configured to construct an initial prediction model for the vehicle's driving range in response to the preprocessed running data, wherein the initial prediction model is used to predict the vehicle's driving range. The initial prediction model optimization module is configured to dynamically adjust the output of the initial prediction model based on the proportional element in the PID algorithm, so as to improve the prediction accuracy of the initial prediction model. The battery capacity optimization and adjustment module is configured to adjust the battery capacity based on the vehicle usage data and the battery status data, including battery health status monitoring, battery capacity calibration, charging strategy optimization, and battery usage mode adjustment. The driving range calculation and output module is configured to combine the optimized initial prediction model and the adjusted battery capacity information to calculate the current driving range of the vehicle and output it to the driver. The initial prediction model optimization steps include dynamically adjusting the output of the initial prediction model based on the proportional element in the PID algorithm to improve the prediction accuracy of the initial prediction model, and further include: The proportional coefficient of the proportional element is dynamically adjusted based on the error between the output of the initial prediction model calculated in real time and the actual driving situation. The adjusted proportional coefficient is applied to the calculation formula of the proportional element to generate a new control quantity; The new control variable is applied to the initial prediction model, and the model output is adjusted in real time to improve prediction accuracy.

7. An electronic device, characterized in that, include: The system includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of the method according to any one of claims 1 to 5.

9. A vehicle, characterized in that, The vehicle is equipped with an electric vehicle range prediction and battery capacity management system as described in claim 6.

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