Electric vehicle electric quantity driving range evaluation method

Abstract

The invention provides an electric vehicle electric quantity driving range evaluation method, which is applied to an electric vehicle electric quantity driving range evaluation system, and the system comprises an electric quantity acquisition module, an electric quantity analysis module, a driving range evaluation module, a prompt module and an optimization module, the method comprises the following steps: S1, acquiring electric quantity data of the electric vehicle through an electric quantity acquisition module; s2, analyzing the obtained electric quantity data through an electric quantity analysis module, and obtaining an electric quantity analysis result; s3, a follow-up evaluation module performs follow-up mileage evaluation calculation according to the electric quantity analysis result; s4, a prompting module carries out driving mileage prompting according to driving mileage evaluation calculation; and S5, an optimization module optimizes an automobile use strategy according to the evaluated and calculated driving mileage. According to the method, by distinguishing different electric quantity conditions of the automobile and analyzing the different electric quantity conditions, accurate evaluation of the electric quantity driving range of the electric vehicle is obtained, and therefore the accuracy and reliability of the evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery driving range assessment, and in particular to a method for assessing the battery driving range of an electric vehicle. Background Art

[0002] At present, with the popularity of electric vehicles, users have an increasing demand for the evaluation of the driving range of electric vehicles. In the prior art, the evaluation of the driving range of electric vehicles is mostly based on static parameters such as battery voltage and capacity, ignoring the impact of dynamic factors such as battery attenuation rate, driving conditions, ambient temperature and user driving habits on the driving range, resulting in inaccurate evaluation results. Static parameter evaluation: The driving range evaluation of most electric vehicles on the market currently mainly relies on static parameters of the battery, such as voltage and capacity. However, these parameters cannot fully reflect the power consumption of electric vehicles during actual driving, because the power consumption is also affected by a variety of dynamic factors. Ignoring dynamic factors: Existing evaluation methods often ignore the impact of dynamic factors such as battery attenuation rate, driving conditions, ambient temperature and user driving habits on power consumption. Due to the neglect of the above factors, the driving range obtained by the existing evaluation method often has a large deviation from the actual driving situation, which brings inconvenience and trouble to users. In actual use, users may face the risk of running out of power due to inaccurate power estimation, affecting driving safety and user experience. A method for evaluating the battery driving range of electric vehicles is proposed to solve the above problems. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a method for evaluating the driving range of an electric vehicle to at least solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A method for evaluating the battery life of an electric vehicle is applied to an evaluation system for the battery life of an electric vehicle. The system includes a battery acquisition module, a battery analysis module, a battery life evaluation module, a prompt module, and an optimization module. The method includes the following steps:

[0006] S1. Obtaining the power data of the electric vehicle through a power acquisition module;

[0007] S2. Analyze the acquired power data through the power analysis module and obtain the power analysis result;

[0008] S3, the range evaluation module performs range evaluation calculation based on the power analysis result;

[0009] S4, the prompt module provides a mileage reminder based on the mileage evaluation calculation;

[0010] S5. The optimization module optimizes the vehicle use strategy based on the estimated and calculated range.

[0011] Furthermore, in step S1, the following sub-steps are included:

[0012] S11, the power acquisition module sets a low power threshold and a saturated power threshold;

[0013] S12, the power acquisition module acquires the power data when the car is started;

[0014] S13. If the car starting power data is lower than the low power threshold, low power type data is obtained; if the car starting power data is between the low power threshold and the saturated power threshold and includes data at both end points of the low power threshold and the saturated power threshold, medium power type data is obtained; if the car starting power data is higher than the saturated power threshold, high power type data is obtained.

[0015] Furthermore, in step S2, the following sub-steps are also included:

[0016] S21, building a machine learning model through the power analysis module;

[0017] S22, the power analysis module obtains the historical driving data of the vehicle and inputs it into the machine learning model for machine learning;

[0018] S23. During the driving of the electric vehicle, the machine learning model analyzes the low-power type data, the medium-power type data, and the high-power type data through time series analysis technology, and obtains the low-power future power consumption analysis results, the medium-power future power consumption analysis results, and the high-power future power consumption analysis results respectively.

[0019] Further, in step S3, the range evaluation module performs range evaluation calculation based on the power analysis result; specifically:

[0020] The range assessment module predicts the future range of low battery based on the analysis results of future power consumption under low battery conditions and the current location, destination or preset driving distance of the vehicle;

[0021] The range assessment module estimates the future range of the vehicle based on the analysis results of the vehicle's future power consumption and the vehicle's current location, destination or preset driving distance.

[0022] The range assessment module predicts the future range of high-power battery based on the analysis results of future power consumption of high-power battery, as well as the current position, destination or preset driving distance of the car.

[0023] Further, in step S4, the prompt module performs a continuous driving range prompt based on the continuous driving range evaluation calculation as follows:

[0024] The prompt module provides an immediate prompt through the instrument panel display or voice prompt based on the predicted low battery and estimated future driving range;

[0025] The prompt module estimates the future driving range based on the predicted power level and gives timely reminders through dashboard display or sound prompts;

[0026] The prompt module evaluates the future range based on the predicted high battery level and gives an optimistic prompt through the instrument panel display or sound prompt.

[0027] Further, in step S5, the optimization module optimizes the vehicle use strategy according to the evaluated and calculated driving range, specifically:

[0028] The optimization module estimates the future driving range based on the predicted low battery and recommends a strategy for finding a charging station as soon as possible, and plans the location and path of nearby charging piles.

[0029] The optimization module evaluates the future driving range based on the predicted battery level and simulates the vehicle usage strategy based on the current driving behavior and the preset driving route;

[0030] The optimization module makes timely charging recommendations and vehicle usage strategies based on the predicted high battery level and future driving range.

[0031] Furthermore, the vehicle driving data includes battery degradation rate, driving conditions, ambient temperature and user driving habits.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention proposes a method for evaluating the battery range of an electric vehicle. The method distinguishes different battery conditions of the vehicle and analyzes the different battery conditions to obtain an accurate evaluation of the battery range of the electric vehicle, thereby improving the accuracy and reliability of the evaluation result, and can also more accurately predict the battery range of the electric vehicle, so as to help users make more reasonable itinerary planning and reduce the risk of driving interruption due to insufficient battery estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1It is a schematic diagram of the overall process of an electric vehicle battery driving range evaluation method proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0037] Reference Figure 1 The present invention provides an electric vehicle battery driving range evaluation method, the method is applied to an electric vehicle battery driving range evaluation system, the system includes a battery acquisition module, a battery analysis module, a driving range evaluation module, a prompt module and an optimization module, the method includes the following steps:

[0038] S1. Obtaining the power data of the electric vehicle through a power acquisition module;

[0039] S2. Analyze the acquired power data through the power analysis module and obtain the power analysis result;

[0040] S3, the range evaluation module performs range evaluation calculation based on the power analysis result;

[0041] S4, the prompt module provides a mileage reminder based on the mileage evaluation calculation;

[0042] S5. The optimization module optimizes the vehicle use strategy based on the estimated and calculated range.

[0043] Exemplarily, the power data of the electric vehicle is obtained through the power acquisition module so as to obtain the current power status of the electric vehicle in real time and accurately; the power analysis module analyzes the obtained power data and obtains the power analysis results so as to deeply process the power data and extract information useful for endurance evaluation; the range evaluation module performs a range evaluation calculation based on the power analysis results so as to predict the range of the electric vehicle in the current state based on the power analysis results; the prompt module performs a range prompt based on the range evaluation calculation so as to present the range evaluation results to the driver in an intuitive and easy-to-understand manner so as to adjust the driving plan or charging strategy in time; the optimization module optimizes the car use strategy based on the calculated range so as to ensure driving safety and improve user experience by optimizing the car use strategy under the premise of ensuring driving needs.

[0044] In step S1, the following sub-steps are included:

[0045] S11, the power acquisition module sets a low power threshold and a saturated power threshold;

[0046] S12, the power acquisition module acquires the power data when the car is started;

[0047] S13. If the car starting power data is lower than the low power threshold, low power type data is obtained; if the car starting power data is between the low power threshold and the saturated power threshold and includes data at both end points of the low power threshold and the saturated power threshold, medium power type data is obtained; if the car starting power data is higher than the saturated power threshold, high power type data is obtained.

[0048] Exemplarily, the power acquisition module can preset a low power threshold and a saturated power threshold according to the characteristics and usage requirements of the electric vehicle. The low power threshold is a lower power level. When the power of the electric vehicle is lower than this threshold, it is considered that the power is insufficient and specific measures need to be taken to ensure that the vehicle can safely drive to the next charging point. The saturated power threshold is a higher power level, usually close to or reaching the maximum capacity of the battery. When the power of the electric vehicle is higher than this threshold, the charging strategy will be adjusted or other car usage suggestions will be provided to avoid damage to the battery caused by overcharging. Each time the electric vehicle is started, the power acquisition module will obtain information from the battery management system (BMS) or other power sensor interface. The current power data is read at the start-up time. The power acquisition module compares the power data at startup with the preset low power threshold and saturation power threshold to determine the power category. If the car startup power data is lower than the low power threshold, it is considered that the current power is very low, which may affect the vehicle's driving range and safety. Therefore, such data is marked as low power type data. If the car startup power data is between the low power threshold and the saturation power threshold, including the two end point data, it is considered that the current power is at a moderate level, neither too high nor too low. Therefore, such data is marked as medium power type data. If the car startup power data is higher than the saturation power threshold, such data is marked as high power type data.

[0049] In step S2, the following sub-steps are also included:

[0050] S21, building a machine learning model through the power analysis module;

[0051] S22, the power analysis module obtains the historical driving data of the vehicle and inputs it into the machine learning model for machine learning;

[0052] S23. During the driving of the electric vehicle, the machine learning model analyzes the low-power type data, the medium-power type data, and the high-power type data through time series analysis technology, and obtains the low-power future power consumption analysis results, the medium-power future power consumption analysis results, and the high-power future power consumption analysis results respectively.

[0053] Exemplarily, the power analysis module is responsible for building and training one or more machine learning models. The selection and construction of the model should be based on the characteristics of the electric vehicle's power consumption and the availability of historical driving data. Common machine learning models include linear regression, decision trees, random forests, neural networks, etc. The specific selection depends on the complexity of the prediction problem and the characteristics of the data. In order to train the machine learning model, the power analysis module collects and organizes the historical driving data of the electric vehicle. These data include but are not limited to influencing factors such as battery decay rate, driving conditions, ambient temperature, and user driving habits. By inputting these data into the machine learning model, the model can learn and identify the complex relationship between power consumption and various factors. During the machine learning process, the model will continuously adjust its internal parameters to minimize the prediction error and optimize the prediction performance. This process may require multiple iterations until the model achieves satisfactory prediction accuracy. During the driving of the electric vehicle, the machine learning model uses time series analysis technology to predict low power. The power type data, medium power type data, and high power type data are analyzed, and the low power future power consumption analysis results, medium power future power consumption analysis results, and high power future power consumption analysis results are obtained respectively. When the machine learning model training is completed and reaches a certain prediction accuracy, during driving, the power analysis module will obtain the current power data in real time, and classify it into low power, medium power or high power type data according to the power threshold. The machine learning model further analyzes these classified power data by using time series analysis technology. The time series analysis technology can capture the trend and law of power consumption over time, so as to more accurately predict future power consumption. For low power, medium power, and high power type data, the machine learning model will make predictions respectively, and output the corresponding future power consumption analysis results, so as to help the driver or the system better understand the vehicle's power consumption, and make more reasonable driving decisions or charging plans.

[0054] In step S3, the range evaluation module performs range evaluation calculation based on the power analysis result; specifically:

[0055] The range assessment module predicts the future range of low battery based on the analysis results of future power consumption under low battery conditions and the current location, destination or preset driving distance of the vehicle;

[0056] The range assessment module estimates the future range of the vehicle based on the analysis results of the vehicle's future power consumption and the vehicle's current location, destination or preset driving distance.

[0057] The range assessment module predicts the future range of high-power battery based on the analysis results of future power consumption of high-power battery, as well as the current position, destination or preset driving distance of the car.

[0058] Exemplarily, when the electric vehicle is in a low-battery state, the range assessment module obtains the future power consumption analysis results of low-battery battery, which reflects the vehicle's power consumption under different driving conditions when the battery is low. The range assessment module predicts the future range based on the vehicle's current position, destination or preset driving distance and the future power consumption analysis results of low-battery battery. The range assessment module obtains the future power consumption analysis results of medium-battery battery, which reflects the vehicle's power consumption under different driving conditions when the battery is medium. The range assessment module also predicts the future range based on the vehicle's current state position, destination or preset driving distance and the future power consumption analysis results of medium-battery battery. In a high-battery state, the range assessment module reflects the vehicle's power consumption under different driving conditions when the battery is high by obtaining the future power consumption analysis results of high-battery battery.

[0059] In step S4, the prompt module performs a continuous driving range prompt based on the continuous driving range evaluation calculation as follows:

[0060] The prompt module provides an immediate prompt through the instrument panel display or voice prompt based on the predicted low battery and estimated future driving range;

[0061] The prompt module estimates the future driving range based on the predicted power level and gives timely reminders through dashboard display or sound prompts;

[0062] The prompt module evaluates the future range based on the predicted high battery level and gives an optimistic prompt through the instrument panel display or sound prompt.

[0063] For example, when the range assessment module predicts that the future range is low in a low-battery state, the prompt module will take immediate action, and can attract the driver's attention through a striking display on the dashboard (such as a red or yellow battery indicator light, a flashing remaining range number, etc.). At the same time, if the vehicle is equipped with a sound prompt function, it will also issue an alarm or voice prompt to clearly inform the driver that the current battery is low and that he needs to find a charging station as soon as possible to charge. This immediate prompt is intended to ensure that the driver can take necessary measures before the battery is exhausted to avoid the vehicle breaking down due to low battery; for the prediction results of the medium battery state, the prompt module will take a gentler but equally effective way to remind. It may display the current battery status through a battery indicator light on the dashboard (such as a green or yellow light), and remind the driver of the battery status through sound or text prompts when appropriate (such as when the remaining mileage approaches a preset threshold). This timely reminder is intended to allow the driver to start considering a charging plan when the battery is relatively sufficient, to ensure that a charging station can be found for charging when needed, to avoid the embarrassing situation of sudden battery exhaustion; when the range assessment module predicts that the future range will be longer in a high battery state, the prompt module will convey this information to the driver in an optimistic manner. It may show the adequacy of the current battery through a green battery indicator light and a larger remaining mileage number on the dashboard. Although there is no need for overly urgent prompts in this case, the prompt module can still encourage drivers to enjoy the fun of long-distance driving through sound or text prompts, and remind them to keep the battery sufficient in case of emergency.

[0064] In step S5, the optimization module optimizes the vehicle use strategy according to the estimated and calculated driving range:

[0065] The optimization module estimates the future driving range based on the predicted low battery and recommends a strategy for finding a charging station as soon as possible, and plans the location and path of nearby charging piles.

[0066] The optimization module evaluates the future driving range based on the predicted battery level and simulates the vehicle usage strategy based on the current driving behavior and the preset driving route;

[0067] The optimization module makes timely charging recommendations and vehicle usage strategies based on the predicted high battery level and future driving range.

[0068] For example, when it is predicted that the future range of low battery evaluation is short, the optimization module will immediately start the emergency response mechanism, that is, it is recommended that the driver find a charging station as soon as possible, and give priority to those charging stations that are close, fast charging, and currently idle. In order to facilitate the driver, the optimization module will also use the map service to plan the optimal route to the relevant nearby charging piles, including considering traffic conditions, road types (such as highways, city streets, etc.) and possible charging station queues. In addition, the optimization module may also reserve charging positions in advance through the communication interface between the vehicle and the charging station to reduce waiting time; for the situation of medium battery evaluation of future range, the optimization module will adopt a more detailed and forward-looking strategy, and conduct scenario simulation analysis based on the driver's current driving behavior (such as speed, acceleration, braking frequency, etc.) and the preset driving route. By simulating the power consumption under different driving behaviors and route selections, the optimization module can predict the most economical driving method and route, and make corresponding suggestions to the driver. For example, it can be recommended that the driver drive at a constant speed on a flat road section to avoid sudden acceleration and sudden braking; slow down appropriately before driving to an uphill section to reduce power consumption. In addition, the optimization module may also provide drivers with mid-trip charging recommendations based on road conditions and the distribution of charging stations to ensure sufficient power; when a high power estimate is expected to lead to a long range in the future, the optimization module will not ignore the importance of power management. On the contrary, timely charging recommendations can be made to drivers to ensure that the power is always kept in the best condition. These recommendations may be based on a variety of factors, such as the health of the battery, the promotions offered by the charging station, and the driver's travel plans. The optimization module may recommend that drivers charge when the power drops to a certain level (such as 80% or 70%) to avoid the impact of excessive discharge on battery life. At the same time, it can also pay attention to the real-time information of charging stations and recommend charging stations with fast charging speeds, favorable prices, and high-quality services to drivers.

[0069] Vehicle driving data includes battery degradation rate, driving conditions, ambient temperature and user driving habits.

[0070] Battery attenuation refers to the reduction in battery capacity due to internal chemical reactions, physical wear and tear during use. This factor is crucial for predicting the power consumption of electric vehicles because as the battery ages, its available energy will gradually decrease. Driving conditions include road conditions (such as urban roads, highways, mountain roads, etc.), traffic congestion, vehicle speed, acceleration, etc. These conditions will directly affect the energy consumption of electric vehicles; ambient temperature has a significant impact on battery performance. At low temperatures, the available energy of the battery will decrease, while at high temperatures, the efficiency and life of the battery may be impaired; user driving habits include acceleration methods, braking frequency, driving speed preferences, etc., which will directly affect the power consumption of electric vehicles; in the training data of the machine learning model, battery attenuation rate, driving conditions, ambient temperature and user driving habits should be included as feature variables. By collecting power consumption data under different battery attenuation rates, driving conditions, ambient temperatures and user driving habits, the model can learn the relationship between battery attenuation and power consumption and take this factor into account when predicting.

[0071] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the battery life of an electric vehicle, characterized in that: The method is applied to an electric vehicle battery driving range evaluation system, the system comprising a battery acquisition module, a battery analysis module, a driving range evaluation module, a prompt module and an optimization module, and the method comprises the following steps: S1. Obtaining the power data of the electric vehicle through a power acquisition module; S2. Analyze the acquired power data through the power analysis module and obtain the power analysis result; S3, the range evaluation module performs range evaluation calculation based on the power analysis result; S4, the prompt module provides a mileage reminder based on the mileage evaluation calculation; S5. The optimization module optimizes the vehicle use strategy based on the estimated and calculated range.

2. The method for evaluating the electric vehicle's battery life according to claim 1, characterized in that: In step S1, the following sub-steps are included: S11, the power acquisition module sets a low power threshold and a saturated power threshold; S12, the power acquisition module acquires the power data when the car is started; S13. If the car starting power data is lower than the low power threshold, low power type data is obtained; if the car starting power data is between the low power threshold and the saturated power threshold and includes data at both end points of the low power threshold and the saturated power threshold, medium power type data is obtained; if the car starting power data is higher than the saturated power threshold, high power type data is obtained.

3. The method for evaluating the electric vehicle's battery life range according to claim 1, characterized in that: In step S2, the following sub-steps are also included: S21, building a machine learning model through the power analysis module; S22, the power analysis module obtains the historical driving data of the vehicle and inputs it into the machine learning model for machine learning; S23. During the driving of the electric vehicle, the machine learning model analyzes the low-power type data, the medium-power type data, and the high-power type data through time series analysis technology, and obtains the low-power future power consumption analysis results, the medium-power future power consumption analysis results, and the high-power future power consumption analysis results respectively.

4. The method for evaluating the electric vehicle's battery life range according to claim 3, characterized in that: In step S3, the range evaluation module performs range evaluation calculation based on the power analysis result; specifically: The range assessment module predicts the future range of low battery based on the analysis results of future power consumption under low battery conditions and the current location, destination or preset driving distance of the vehicle; The range assessment module predicts the range of the vehicle based on the analysis results of the vehicle's future power consumption and the vehicle's current location, destination or preset driving distance. The range assessment module predicts the future range of high-power battery based on the analysis results of future power consumption of high-power battery, as well as the current position, destination or preset driving distance of the car.

5. The method for evaluating the electric vehicle's battery life range according to claim 4, characterized in that: In step S4, the prompt module performs a continuous driving range prompt based on the continuous driving range evaluation calculation as follows: The prompt module provides an immediate prompt through the instrument panel display or voice prompt based on the predicted low battery and estimated future driving range; The prompt module estimates the future driving range based on the predicted power level and gives timely reminders through dashboard display or sound prompts; The prompt module evaluates the future range based on the predicted high battery level and gives an optimistic prompt through the instrument panel display or sound prompt.

6. The method for evaluating the electric vehicle's battery life range according to claim 5, characterized in that: In step S5, the optimization module optimizes the vehicle use strategy according to the estimated and calculated driving range: The optimization module estimates the future driving range based on the predicted low battery and recommends a strategy for finding a charging station as soon as possible, and plans the location and path of nearby charging piles. The optimization module evaluates the future driving range based on the predicted battery level and simulates the vehicle usage strategy based on the current driving behavior and the preset driving route; The optimization module makes timely charging recommendations and vehicle usage strategies based on the predicted high battery level and future driving range.

7. The method for evaluating the electric vehicle's battery life range according to claim 3, characterized in that: Vehicle driving data includes battery degradation rate, driving conditions, ambient temperature and user driving habits.

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