System and method for evaluating dynamic endurance mileage of electric vehicle and vehicle

Abstract

The invention discloses an electric vehicle dynamic endurance mileage evaluation system and method and a vehicle. Real-time traffic data of a vehicle position is acquired through a traffic data acquisition module; the weather data acquisition module acquires real-time weather data of a vehicle environment; the operation data acquisition module acquires operation data of a vehicle; the route planning and display module reads a vehicle driving route plan of a user; the vehicle weight acquisition module acquires the overall weight data of the current vehicle; the vehicle cloud interaction module forwards the received data to the far-end calculation module; real-time interaction between the vehicle end and the cloud end is realized; and the cloud computing module outputs route endurance mileage data through the vehicle endurance model. The current vehicle state, the vehicle weight, the weather, the traffic, the electricity utilization information and the like are subjected to data processing to obtain the electric quantity consumed by the line and the drivable driving mileage, real-time driving mileage calculation in the driving process is achieved, and the credibility of the driving mileage calculation result is improved.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle range prediction technology, specifically relating to an electric vehicle dynamic range assessment system, method, and vehicle. Background Technology

[0002] With the rapid development of electric vehicle technology and the continuous increase in market penetration, short driving range and inconvenient charging remain key factors restricting consumers' purchasing decisions. Currently, the driving range displayed for electric vehicles is usually based on laboratory test values ​​under standard driving conditions (such as NEDC, CLTC, and WLTC) or estimated using historical driving energy consumption data. These methods fail to fully reflect the dynamic changes in real-world driving scenarios, leading to a significant difference between the displayed and actual driving range. This exacerbates users' range anxiety and causes unnecessary redundant charging.

[0003] In existing technologies, several solutions have emerged aimed at improving the accuracy of range prediction. For example, patent CN118544883A proposes a method and system for predicting the range of electric vehicles. This method improves prediction accuracy to some extent by introducing multi-dimensional factors such as driver type, vehicle model, load range, and season to construct a prediction model. The core of this solution lies in dividing the route into multiple units and matching each unit with a standard driving segment. A pre-trained range prediction model is then used to estimate energy consumption. However, this method still has certain limitations: First, it relies on a pre-established standardized segment library and a model trained on historical data, making it less adaptable to real-time traffic changes, sudden road conditions, or weather fluctuations. Second, it does not fully consider the close coupling between real-time energy consumption data and vehicle dynamics, limiting the real-time nature of the prediction and its dynamic adjustment capabilities.

[0004] A new range assessment system is needed to address the aforementioned technical issues. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic driving range evaluation system for electric vehicles, which solves the technical problems in the prior art that electric vehicles cannot achieve real-time range prediction and that the real-time prediction results are inaccurate.

[0006] Another objective of this invention is to provide an evaluation method applicable to a dynamic driving range evaluation system for electric vehicles.

[0007] Another objective of this invention is to provide a vehicle.

[0008] The technical solution of this invention to solve its technical problem is as follows: A dynamic driving range evaluation system for electric vehicles, comprising: The traffic data acquisition module is used to collect real-time traffic data of vehicle locations and send the traffic data to the vehicle-cloud interaction module. The weather data acquisition module is used to collect real-time weather data of the vehicle environment and send the weather data to the vehicle-cloud interaction module. The data acquisition module is used to collect vehicle operation data and send the vehicle operation data to the vehicle-cloud interaction module. The route planning and display module is used to read the user's vehicle driving route plan, send the vehicle driving route plan to the vehicle-cloud interaction module, receive the route range data output by the vehicle-cloud interaction module, and display the route range data after conversion. The vehicle weight acquisition module is used to collect the overall weight data of the current vehicle and send the vehicle weight data to the vehicle-cloud interaction module. The vehicle-cloud interaction module is used to receive and forward data sent by the traffic data collection module, weather data collection module, operation data collection module, route planning and display module, vehicle weight collection module, and cloud computing module, so as to realize real-time interaction between the vehicle and the cloud. The cloud computing module is used to input real-time traffic data of vehicle location, real-time weather data of vehicle environment, vehicle operation data, user vehicle driving route planning, and overall vehicle weight data into the vehicle range model. Based on the weight ratio of each part of the data, the vehicle range model outputs route range data and sends the route range data to the vehicle-cloud interaction module. The vehicle range model adopts a convolutional neural network model and is trained based on historical vehicle data.

[0009] Preferably, the cloud computing module further includes a cloud database for storing structured historical data of the vehicle.

[0010] Preferably, the structured historical data specifically includes: interval vehicle speed trajectory, battery status parameters, accessory energy consumption, ambient temperature and humidity, time and driving style characteristics.

[0011] Preferably, the objective function of the vehicle range model is: ; ; ; ; ; ; ; in, Indicates the current remaining battery level of the vehicle; This indicates the electricity consumption required for a vehicle to travel a short distance along a narrow road segment. These respectively indicate that the vehicle is in Electricity consumption required for short road sections; This indicates the planned driving range for the vehicle's route. This indicates the mileage of the smaller route segments into which the vehicle's driving route is planned. This represents the mileage of 1 to n smaller route intervals into which the vehicle's driving route is planned. This indicates the power required for a vehicle to traverse a short section of road. These respectively indicate that the vehicle is in Power required for short road sections; Represents real-time weather factors, specifically as , corresponding to 1-L different weather data real-time weather factors, obtained based on the empirical values ​​of similar weather factors in the database; This indicates the vehicle's speed over a short road segment, specifically expressed as... , The route factor is specifically represented as The route factors corresponding to each sub-route interval are obtained based on historical experience data of vehicles with similar driving styles in the database; The weight factor is specifically represented as The weight factor corresponding to each sub-route interval is obtained from the weight acquisition module; This indicates the travel time for the corresponding section of road. These correspond to the travel time for each section of the route. This indicates the time required for the planned vehicle route. This indicates the time required to cover the specified distance on the route. , , .

[0012] Preferably, the real-time traffic data for the vehicle location includes: route speed limits, congestion conditions, and traffic lights.

[0013] Preferably, the real-time weather data of the vehicle environment includes: temperature, weather, and wind speed.

[0014] Preferably, the vehicle's operating data includes: battery parameters, drive motor system parameters, and accessory power consumption data.

[0015] An evaluation method for a dynamic driving range evaluation system for electric vehicles includes the following steps: S1: Construct a vehicle range model that includes functions related to real-time traffic data, real-time weather data, vehicle operation data, user vehicle route planning, and the overall weight of the vehicle. S2: The cloud computing module determines whether the vehicle is started. If so, the traffic data acquisition module collects real-time traffic data of the vehicle's location and sends it to the vehicle-cloud interaction module; the weather data acquisition module collects real-time weather data of the vehicle's environment and sends it to the vehicle-cloud interaction module; the operation data acquisition module collects the vehicle's operation data and sends it to the vehicle-cloud interaction module; the route planning and display module reads the user's vehicle driving route plan and sends it to the vehicle-cloud interaction module; the vehicle weight acquisition module collects the current overall weight data of the vehicle and sends it to the vehicle-cloud interaction module; the vehicle-cloud interaction module forwards the received data to the remote computing module; if not, the current state is maintained. S3: The remote computing module determines whether it has received the user's vehicle route planning data. If so, it divides the vehicle route into a set number of smaller route segments, calls the structured historical data of the vehicle in the cloud database, and retrieves the vehicle data with the highest similarity to the current vehicle status and driving style in each segment. Using weighted coefficients, it integrates real-time traffic data of the vehicle's location, real-time weather data of the vehicle's environment, vehicle operation data, and overall vehicle weight data through the vehicle range model, outputs the route range data corresponding to the vehicle route planning, and displays it through the route planning and display module after being forwarded by the vehicle-cloud interaction module. If not, it maintains the current state.

[0016] Preferably, the vehicle range model in step S3 further includes energy consumption correction before outputting the route range data. Specifically, energy consumption correction is performed by removing outliers. It is determined whether the cumulative energy consumption of each segment in the current vehicle's driving route is not greater than the total battery power of the current vehicle. If so, the route range data corresponding to the vehicle's driving route is output and displayed through the route planning and display module after being forwarded by the vehicle-cloud interaction module. If not, the mileage that the vehicle can travel and the route location that can be reached are output and displayed through the route planning and display module after being forwarded by the vehicle-cloud interaction module.

[0017] A vehicle that uses an electric vehicle dynamic range assessment system.

[0018] The beneficial effects of this invention are as follows: By setting up modules for traffic data acquisition, weather data acquisition, operational data acquisition, route planning and display, vehicle weight acquisition, vehicle-to-cloud interaction, and cloud computing, the system achieves real-time interaction between the vehicle and the cloud. The traffic data acquisition module collects real-time traffic data of the vehicle's location and sends it to the vehicle-to-cloud interaction module; the weather data acquisition module collects real-time weather data of the vehicle's environment and sends it to the vehicle-to-cloud interaction module; the operational data acquisition module collects vehicle operational data and sends it to the vehicle-to-cloud interaction module; the route planning and display module reads the user's vehicle route plan and sends it to the vehicle-to-cloud interaction module; and the vehicle weight acquisition module collects the current overall weight data of the vehicle and sends it to the vehicle-to-cloud interaction module. The vehicle-to-cloud interaction module forwards the received data to the remote computing module, enabling real-time interaction between the vehicle and the cloud. The cloud computing module inputs real-time traffic data of the vehicle's location, real-time weather data of the vehicle's environment, vehicle operational data, the user's vehicle route plan, and the overall weight data of the vehicle into the vehicle range model. Based on the weight ratio of each data component, the vehicle range model outputs route range data, which is then forwarded by the vehicle-to-cloud interaction module and displayed through the route planning and display module. This cloud-based digital modeling of the vehicle generates real-time vehicle twin data. Furthermore, by calling historical vehicle driving data of the target route in the database, and combining it with current vehicle status, vehicle weight, weather, traffic, electricity consumption information, etc., the system processes the data to obtain the electricity consumed and driving range of the section of the route, thereby realizing real-time and accurate range calculation of electric vehicles during driving and improving the reliability of the range calculation results. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of the electric vehicle dynamic range evaluation system of the present invention; Figure 2 This is a flowchart illustrating the evaluation method of the present invention applied to the dynamic driving range evaluation system for electric vehicles. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0021] like Figure 1 As shown, this invention discloses a dynamic driving range evaluation system for electric vehicles, comprising: The traffic data acquisition module is used to collect real-time traffic data of vehicle location and send the traffic data to the vehicle-cloud interaction module; the real-time traffic data of vehicle location includes: route speed limit, congestion status, and traffic lights.

[0022] The weather data acquisition module is used to collect real-time weather data of the vehicle environment and send the weather data to the vehicle-cloud interaction module; the real-time weather data of the vehicle environment includes: temperature, weather, and wind speed.

[0023] The operation data acquisition module is used to collect vehicle operation data and send the vehicle operation data to the vehicle-cloud interaction module; the vehicle operation data includes: battery parameters, drive motor system parameters, and accessory power consumption data.

[0024] The route planning and display module is used to read the user's vehicle driving route plan, send the vehicle driving route plan to the vehicle-cloud interaction module, receive the route range data output by the vehicle-cloud interaction module, and display the route range data after conversion. The vehicle weight acquisition module is used to collect the overall weight data of the current vehicle and send the vehicle weight data to the vehicle-cloud interaction module. The vehicle-cloud interaction module is used to receive and forward data sent by the traffic data collection module, weather data collection module, operation data collection module, route planning and display module, vehicle weight collection module, and cloud computing module, so as to realize real-time interaction between the vehicle and the cloud. The cloud computing module inputs real-time traffic data of the vehicle's location, real-time weather data of the vehicle's environment, vehicle operation data, user's vehicle route planning, and overall vehicle weight data into the vehicle range model. Based on the weighted proportions of each data component, the vehicle range model outputs route range data and sends it to the vehicle-cloud interaction module. The cloud computing module also includes a cloud database for storing structured historical data of the vehicle, specifically including: interval vehicle speed trajectory, battery status parameters, accessory energy consumption, ambient temperature and humidity, and time and driving style characteristics. The vehicle range model is a convolutional neural network model trained based on the vehicle's historical data.

[0025] The objective function of the vehicle range model is: ; ; ; ; ; ; ; in, Indicates the current remaining battery level of the vehicle; This indicates the electricity consumption required for a vehicle to travel a short distance along a narrow road segment. These respectively indicate that the vehicle is in Electricity consumption required for short road sections; This indicates the planned driving range for the vehicle's route. This indicates the mileage of the smaller route segments into which the vehicle's driving route is planned. This represents the mileage of 1 to n smaller route intervals into which the vehicle's driving route is planned. This indicates the power required for a vehicle to traverse a short section of road. These respectively indicate that the vehicle is in Power required for short road sections; Represents real-time weather factors, specifically as , corresponding to 1-L different weather data real-time weather factors, obtained based on the empirical values ​​of similar weather factors in the database; This indicates the vehicle's speed over a short road segment, specifically expressed as... , The route factor is specifically represented as The route factors corresponding to each sub-route interval are obtained based on historical experience data of vehicles with similar driving styles in the database; The weight factor is specifically represented as The weight factor corresponding to each sub-route interval is obtained from the weight acquisition module; This indicates the travel time for the corresponding section of road. These correspond to the travel time for each section of the route. This indicates the time required for the planned vehicle route. This indicates the time required to cover the specified distance on the route. , , .

[0026] This system does not require road surface information; it only needs to obtain the power consumption information for the current vehicle's target route by comparing it with the historical driving data of the most similar vehicles in the database and making data corrections based on the differences. Furthermore, as the vehicle information in the database is continuously enriched, the vehicle information retrieved becomes more accurate, and the vehicle's range and power consumption predictions become more precise.

[0027] An evaluation method for a dynamic driving range evaluation system for electric vehicles includes the following steps: S1: Construct a vehicle range model that includes functions related to real-time traffic data, real-time weather data, vehicle operation data, user vehicle route planning, and the overall weight of the vehicle. S2: The cloud computing module determines whether the vehicle is started. If so, the traffic data acquisition module collects real-time traffic data of the vehicle's location and sends it to the vehicle-cloud interaction module; the weather data acquisition module collects real-time weather data of the vehicle's environment and sends it to the vehicle-cloud interaction module; the operation data acquisition module collects the vehicle's operation data and sends it to the vehicle-cloud interaction module; the route planning and display module reads the user's vehicle driving route plan and sends it to the vehicle-cloud interaction module; the vehicle weight acquisition module collects the current overall weight data of the vehicle and sends it to the vehicle-cloud interaction module; the vehicle-cloud interaction module forwards the received data to the remote computing module; if not, the current state is maintained. S3: The remote computing module determines whether it has received the user's vehicle route planning data. If so, it divides the vehicle route into a set number of smaller route segments, calls the structured historical data of the vehicle in the cloud database, and retrieves the vehicle data with the highest similarity to the current vehicle status and driving style in each segment. Using weighted coefficients, it integrates real-time traffic data of the vehicle's location, real-time weather data of the vehicle's environment, vehicle operation data, and overall vehicle weight data through the vehicle range model, outputs the route range data corresponding to the vehicle route planning, and displays it through the route planning and display module after being forwarded by the vehicle-cloud interaction module. If not, it maintains the current state. The vehicle range model includes energy consumption correction before outputting the route range data. Specifically, energy consumption correction is performed by removing outliers. It is determined whether the cumulative energy consumption of each segment of the current vehicle's route is not greater than the vehicle's total battery power. If so, the route range data corresponding to the planned route is output and displayed through the route planning and display module after being forwarded by the vehicle-cloud interaction module. If not, the mileage that the vehicle can travel and the route location that it can reach are output and displayed through the route planning and display module after being forwarded by the vehicle-cloud interaction module.

[0028] A vehicle that uses an electric vehicle dynamic range assessment system.

[0029] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

Claims

1. A dynamic driving range evaluation system for electric vehicles, characterized in that, The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device.

2. The system for evaluating dynamic range of electric vehicle according to claim 1, wherein: The application relates to a vehicle route endurance data display method and device.

3. The system for evaluating dynamic range of electric vehicle according to claim 2, wherein: The application relates to a vehicle route endurance data display method and device.

4. The system for evaluating dynamic range of electric vehicle according to claim 2, wherein: The application relates to a vehicle route endurance data display method and device. ； ； ； ； ； ； ； wherein, represents the current remaining amount of the vehicle battery; represents the power consumption of the vehicle in the small road section interval, respectively represent the power consumption of the vehicle in the small road section interval; represents the route range of the vehicle driving route planning, represents the small route interval range of the vehicle driving route planning, represents the 1-n small route interval range of the vehicle driving route planning, represents the power consumption of the vehicle in the small road section interval, respectively represent the power consumption of the vehicle in the small road section interval; represents the real-time weather factor, specifically represented as , the real-time weather factor corresponding to 1-L different weather data, obtained according to the experience value of the similar weather factor in the database; represents the vehicle speed in the small road section interval, specifically represented as , represents the route factor, specifically represented as , the route factor corresponding to each small route interval, obtained according to the historical experience value data of the similar driving style vehicle in the database; represents the weight factor, specifically represented as , the weight factor corresponding to each small route interval, obtained by the weight collection module; represents the passing time corresponding to the small road section interval, respectively corresponding to the passing time corresponding to each small route interval, represents the required time corresponding to the vehicle driving route planning, represents the required time corresponding to the route range, , , .

5. The system for evaluating dynamic range of electric vehicle according to claim 1, wherein: The application relates to a vehicle route endurance data display method and device.

6. The system for evaluating dynamic range of electric vehicle according to claim 1, wherein: The application relates to a vehicle route endurance data display method and device.

7. The system for evaluating dynamic range of electric vehicle according to claim 1, wherein: The application relates to a vehicle route endurance data display method and device.

8. An evaluation method applied to the electric vehicle dynamic range evaluation system according to any one of claims 1 to 7, characterized in that, The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. The application relates to a vehicle route endurance data display method and device. 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The application relates to S3: The remote computing module determines whether the user's vehicle driving route planning data is received. If yes, the vehicle driving route is divided into small route intervals of set segments, the structured historical data of the vehicle in the cloud database is called, and the vehicle data with the maximum similarity to the current vehicle state and driving style in each small road segment interval is retrieved. The real-time traffic data of the vehicle location, the real-time weather data of the vehicle environment, the running data of the vehicle, and the weight data of the vehicle are integrated by the vehicle endurance model through the weight coefficient, the route endurance mileage data corresponding to the vehicle driving route planning is output, and the vehicle cloud interaction module is forwarded to display through the route planning and display module. If not, the current state is maintained.

9. The evaluation method applied to the dynamic range evaluation system of the electric vehicle according to claim 8, characterized in that: The vehicle endurance model in step S3 further includes energy consumption correction before outputting the route endurance mileage data. Specifically, the energy consumption correction is performed by removing abnormal values. It is determined whether the energy consumption accumulation result of each small road segment interval in the current vehicle driving route is not greater than the total power of the current vehicle. If yes, the route endurance mileage data corresponding to the vehicle driving route planning is output; if not, the mileage that the vehicle can travel and the route position that the vehicle can reach are output, and the vehicle cloud interaction module is forwarded to display through the route planning and display module. The electric vehicle dynamic endurance mileage evaluation system of any one of claims 1-7 is adopted.

10. A vehicle characterized by: ​

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