Scenarized electric vehicle endurance mileage prediction method, device, equipment and medium

Abstract

The invention discloses a scenarized electric vehicle endurance mileage prediction method and device, equipment and a medium. The method comprises the following steps: acquiring real-time state parameters of a vehicle, real-time environment parameters and navigation path associated information, and calculating the theoretical maximum endurance mileage of the vehicle based on the real-time state parameters; inputting the environment real-time parameters, the navigation path associated information and the historical driving behavior data of the user into a dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor; performing weighted correction on the theoretical maximum endurance mileage based on the multi-dimensional dynamic attenuation factor to obtain a conservative estimated endurance mileage, and constructing a scenarized endurance mileage range in combination with the theoretical maximum endurance mileage; and carrying out visual output on the scenarized endurance mileage range. According to the technical scheme, multi-factor fusion scene endurance accurate prediction is realized, the endurance mileage range fitting the reality is output, the information interpretation threshold is reduced in a visual form, and the endurance anxiety of the user is effectively relieved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle range prediction technology, and in particular to a method, apparatus, device, and medium for predicting the range of electric vehicles in various scenarios. Background Technology

[0002] Currently, the display of driving range for electric vehicles mainly relies on two traditional technologies:

[0003] 1. Estimation based on standard driving cycles (NEDC / CLTC / WLTC): This method calculates the remaining range based on fixed energy consumption values ​​obtained from laboratory standard driving cycle tests, combined with the current battery charge (SOC). This method significantly deviates from actual driving environments, resulting in a large discrepancy between the displayed range and the actual driving range, especially in low-temperature and high-speed scenarios. These include the New European Driving Cycle (NEDC), the China Light-duty Vehicle Test Cycle (CLTC), and the World Light Vehicle Test Cycle (WLTC).

[0004] 2. Estimation based on historical average energy consumption: The remaining mileage is dynamically estimated based on the user's recent (e.g., the first 50 kilometers) average energy consumption. While this method offers some improvement, it still suffers from lag and limitations. It cannot proactively predict specific road conditions (e.g., congestion, gradient), environmental changes (e.g., sudden temperature drops), or the user's driving style for future trips.

[0005] The fundamental flaw of the aforementioned existing technologies is that they convey a single, absolute, and uncertain number to users, forcing them to bear the psychological burden of "mileage discounts" and thus causing serious "range anxiety." Summary of the Invention

[0006] This invention provides a scenario-based electric vehicle range prediction method, device, equipment, and medium to achieve accurate scenario-based range prediction through multi-factor fusion, output a range range that closely matches reality, and lower the information interpretation threshold through visualization, effectively alleviating users' range anxiety and providing accurate and intuitive reference for users' trip planning and charging decisions.

[0007] According to one aspect of the present invention, a scenario-based electric vehicle range prediction method is provided, comprising:

[0008] The vehicle's real-time status parameters, environmental real-time parameters, and navigation path association information are obtained, and the vehicle's theoretical maximum range is calculated based on the real-time status parameters.

[0009] The real-time environmental parameters, the navigation path association information, and the user's historical driving behavior data are input into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor.

[0010] Based on the multi-dimensional dynamic attenuation factor, the theoretical maximum range is weighted and corrected to obtain a conservative estimated range, and a scenario-based range is constructed by combining the theoretical maximum range.

[0011] The scenario-based driving range will be visualized and output.

[0012] According to another aspect of the present invention, a scenario-based electric vehicle range prediction device is provided, comprising:

[0013] The parameter acquisition and mileage calculation module is used to acquire the vehicle's real-time status parameters, real-time environmental parameters, and navigation path association information, and calculate the vehicle's theoretical maximum range based on the real-time status parameters.

[0014] The attenuation factor acquisition module is used to input the real-time environmental parameters, the navigation path association information and the user's historical driving behavior data into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor.

[0015] The range construction module is used to perform weighted correction on the theoretical maximum range based on the multi-dimensional dynamic attenuation factor to obtain a conservative estimated range, and to construct a scenario-based range based on the theoretical maximum range.

[0016] The visualization output module is used to visualize the scenario-based driving range.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor;

[0019] and memory that is communicatively connected to at least one processor;

[0020] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to execute the scenario-based electric vehicle range prediction method of any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the scenario-based electric vehicle range prediction method of any embodiment of the present invention.

[0022] The technical solution of this invention obtains real-time vehicle status parameters, real-time environmental parameters, and navigation path association information, and calculates the vehicle's theoretical maximum range based on the real-time status parameters. It then inputs the real-time environmental parameters, navigation path association information, and user historical driving behavior data into a dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor. Based on the multi-dimensional dynamic attenuation factor, a weighted correction is applied to the theoretical maximum range to obtain a conservatively estimated range. This is combined with the theoretical maximum range to construct a scenario-based range range. The scenario-based range range is then visualized and output. This solves the problems of traditional electric vehicle range prediction, which only outputs a single value, is disconnected from actual driving scenarios, has low prediction accuracy, and lacks intuitive range information delivery, leading to significant user range anxiety and difficulty in accurately planning trips. The solution achieves accurate scenario-based range prediction through multi-factor fusion, outputting a range range that closely matches reality. Furthermore, the visualization format lowers the information interpretation threshold, effectively alleviating user range anxiety and providing accurate and intuitive reference for user trip planning and charging decisions.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a scenario-based electric vehicle range prediction method provided in this embodiment of the invention;

[0026] Figure 2 This is an architecture diagram of a scenario-based electric vehicle range prediction system provided in an embodiment of the present invention;

[0027] Figure 3 A flowchart of another scenario-based electric vehicle range prediction method provided in an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of a scenario-based electric vehicle range prediction device provided in an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the structure of an electronic device for implementing the scenario-based electric vehicle range prediction method of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Among the relevant technologies, there are several methods for predicting the driving range of electric vehicles:

[0033] Option 1 involves acquiring operational data of the target vehicle, including battery state parameters, driving behavior parameters, and ambient temperature parameters. The battery state parameters include the State of Health (SOH). This operational data is used as input to a first random forest model, yielding first predicted values ​​from multiple decision trees within the model. The average of these first predicted values ​​is then calculated, outputting the estimated driving range (ECR) of the target vehicle. The operational data and the ECR predictions are then used as input to a second random forest model, yielding second predicted values ​​from multiple decision trees within the model. The average of these second predicted values ​​is then calculated, outputting the predicted actual driving range (RDR) of the target vehicle. The remaining driving range of the target vehicle is determined based on the RDR predictions. Both the first and second random forest models are trained using sample data, thus improving the accuracy of RDR estimation for electric vehicles. However, Option 1 only predicts the remaining driving range based on parameters such as battery status, which cannot provide users with a "reliable driving range". If the prediction is wrong, it will reduce users' trust. In addition, it does not support the prediction function of "remaining battery power after arrival", so users cannot know for sure whether they can reach their destination before setting off, and thus cannot realize the transformation of users from "anxiety" to "planning".

[0034] Option 2 involves acquiring the first charge / discharge data and first state of charge (SOC) value of the target vehicle's first battery at the current moment; based on the correspondence between the candidate vehicle's second battery and a pre-trained battery SOC prediction model, determining the target SOC prediction model corresponding to the first battery; inputting the first charge / discharge data into the target SOC prediction model to obtain the second SOC value of the first battery at the current moment; and, provided the deviation between the first and second SOC values ​​does not exceed a preset deviation threshold, sending the second SOC value to the target vehicle so that the target vehicle can determine the target SOC value of the first battery at the current moment based on the first and second SOC values, thus achieving a relatively accurate determination of the vehicle battery's SOC. However, Option 2 lacks personalization and adaptability, failing to provide high-precision predictions for specific users and trips based on factors such as driver habits and ambient temperature; it also fails to translate complex technical parameters into easily understandable visual language, thus not reducing the user's cognitive load. It does not utilize "dynamic range loops" and "scenario cards" to make information delivery more user-friendly and efficient.

[0035] In summary, this invention aims to address the problem that existing technologies cannot provide accurate, reliable, and easily understandable range predictions. Specifically, the technical problems this invention aims to solve include at least: 1. How to overcome the limitations of a single mileage value and provide a prediction method that can reflect the real-world range capability under various driving scenarios. 2. How to transform professional battery energy consumption data into visual information that users can intuitively perceive and understand, lowering the information interpretation threshold. 3. How to achieve real-time, dynamic range assessment based on user-defined navigation routes, providing users with pre-trip decision support and in-trip peace of mind. To solve at least one of the technical problems existing in the above-mentioned related technologies, this invention provides a scenario-based electric vehicle range prediction method.

[0036] Figure 1 This is a flowchart illustrating a scenario-based electric vehicle range prediction method provided in an embodiment of the present invention. This embodiment is applicable to situations requiring accurate prediction of electric vehicle range. The method can be executed by a scenario-based electric vehicle range prediction device, which can be implemented in hardware and / or software and can be configured in an electric vehicle. Figure 1 As shown, the method specifically includes the following steps:

[0037] S110: Obtain the vehicle's real-time status parameters, environmental real-time parameters, and navigation path association information, and calculate the vehicle's theoretical maximum range based on the real-time status parameters.

[0038] Among them, the real-time status parameters of the vehicle can be the vehicle's own operating parameters that reflect the current battery status and energy consumption of the electric vehicle; the real-time environmental parameters can be relevant data of the external natural environment in which the vehicle is located; the navigation path association information can be road and road condition data related to the vehicle's planned navigation path; the theoretical maximum range refers to the maximum driving range that the vehicle can achieve based on the current battery status under ideal operating conditions.

[0039] Specifically, real-time vehicle status parameters, real-time environmental parameters, and navigation path association information can be obtained. Then, based on the obtained real-time vehicle status parameters, the vehicle's theoretical maximum range can be calculated through the correlation between energy consumption and battery power.

[0040] S120. Input the real-time environmental parameters, the navigation path association information, and the user's historical driving behavior data into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor.

[0041] Among them, the user's historical driving behavior data can be data related to the user's driving behavior, the dynamic attenuation factor determination model can be a model used to output various attenuation coefficients that affect the vehicle's range, and the multi-dimensional dynamic attenuation factor is a coefficient that reflects the degree of attenuation of the vehicle's range from multiple dimensions.

[0042] Specifically, real-time environmental parameters, navigation path association information, and user historical driving behavior data are input into the dynamic attenuation factor determination model, and the model outputs a multi-dimensional dynamic attenuation factor.

[0043] S130. Based on the multi-dimensional dynamic attenuation factor, the theoretical maximum driving range is weighted and corrected to obtain a conservative estimated driving range, and combined with the theoretical maximum driving range to construct a scenario-based driving range range.

[0044] S140. Visualize and output the scenario-based driving range.

[0045] Among them, the conservatively estimated driving range can be understood as the minimum driving range that the vehicle can achieve under actual comprehensive operating conditions; the scenario-based driving range range can be the driving range range obtained by combining the ideal and actual operating conditions of the vehicle; and the visualization output can be the display of driving range-related data in an intuitive graphic and textual form.

[0046] Specifically, the theoretical maximum range can be weighted and corrected based on a multi-dimensional dynamic attenuation factor to calculate a conservatively estimated range. Then, the theoretical maximum range and the conservatively estimated range are combined to construct a scenario-based range. Finally, the constructed scenario-based range is visualized in an intuitive way, realizing accurate scenario-based range prediction based on multi-factor fusion. The output range is close to reality, and the visualization reduces the information interpretation threshold, effectively alleviating users' range anxiety and providing accurate and intuitive reference for users' trip planning and charging decisions.

[0047] In some possible implementations, acquiring the vehicle's real-time status parameters, real-time environmental parameters, and navigation path association information, and calculating the vehicle's theoretical maximum range based on the real-time status parameters, includes: collecting the vehicle's remaining battery power, battery health status, and real-time energy consumption data as the vehicle's real-time status parameters; collecting external temperature, wind speed, and precipitation probability data as the real-time environmental parameters; collecting navigation-based path information, real-time road conditions, altitude gradient, and historical average vehicle speed data as the navigation path association information; extracting the remaining battery power and battery health status from the real-time status parameters to calculate the actual usable battery pack power, and extracting the vehicle's baseline energy consumption under ideal conditions from the real-time status parameters; and calculating the vehicle's theoretical maximum range based on the ratio of the actual usable battery pack power to the baseline energy consumption.

[0048] Among them, the remaining battery charge can be the proportion of the vehicle's current remaining electrical energy to the total electrical energy; the battery health status is an indicator reflecting the current performance and aging degree of the vehicle's battery; the real-time energy consumption is the electrical energy consumption per unit of driving distance; the external temperature can be the actual temperature of the vehicle's environment; the wind speed is the air flow speed of the vehicle's environment; and the precipitation probability is the likelihood of precipitation occurring in the vehicle's environment within a certain period of time.

[0049] Route information can be data related to the planned driving route; real-time traffic conditions are the current traffic conditions on the navigation route; elevation gradient can be the elevation and inclination of the terrain on the navigation route; historical average speed can be the average speed of vehicles passing by on the navigation route; actual available battery pack power is the actual electrical energy that the vehicle's battery pack can currently provide to the vehicle; and baseline energy consumption is the standard electrical energy consumption per unit distance traveled under ideal conditions.

[0050] When performing the steps of acquiring real-time vehicle status parameters, real-time environmental parameters, and navigation path association information, and calculating the theoretical maximum driving range, the vehicle's remaining battery power, battery health status, and real-time energy consumption data can be collected separately as real-time vehicle status parameters. External temperature, wind speed, and precipitation probability data can be collected as real-time environmental parameters. Navigation-based path information, real-time road conditions, altitude gradient, and historical average vehicle speed data can be collected as navigation path association information. Then, the remaining battery power and battery health status are extracted from the collected real-time status parameters, and the actual usable battery power is calculated through the correlation between the two. At the same time, the baseline energy consumption under ideal conditions is extracted from the real-time status parameters. Finally, the theoretical maximum driving range of the vehicle is accurately calculated based on the ratio of the actual usable battery power to the baseline energy consumption.

[0051] In some possible implementations, the dynamic attenuation factor determination model is a machine learning model pre-trained based on massive amounts of real vehicle data, and the multi-dimensional dynamic attenuation factor includes a temperature attenuation factor, a road condition attenuation factor, and a driving style attenuation factor.

[0052] Among them, massive real vehicle data can be the actual driving data generated by a large number of electric vehicles in different scenarios and under different operating conditions. The machine learning model is an algorithm model that is trained with data and has the ability to process data and output results. The temperature decay factor is a coefficient that reflects the impact of ambient temperature on the vehicle's range decay. The road condition decay factor is a coefficient that reflects the impact of driving road conditions on the vehicle's range decay. The driving style decay factor is a coefficient that reflects the impact of user driving habits on the vehicle's range decay.

[0053] Specifically, the dynamic attenuation factor determination model is set as a machine learning model pre-trained based on massive amounts of real vehicle data. After being trained on massive amounts of real vehicle data, this model can better fit the actual operating conditions of the vehicle. The output multi-dimensional dynamic attenuation factor specifically includes temperature attenuation factor, road condition attenuation factor, and driving style attenuation factor. Considering the range attenuation from three key dimensions of temperature, road condition, and driving style, the range correction can be more in line with the actual driving scenario, improving the accuracy of range prediction.

[0054] In some possible implementations, the step of inputting the real-time environmental parameters, the navigation path association information, and the user's historical driving behavior data into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor includes: inputting the real-time environmental parameters into the dynamic attenuation factor determination model to obtain the temperature attenuation factor; inputting the navigation path association information into the dynamic attenuation factor determination model to obtain the road condition attenuation factor; and inputting the user's historical driving behavior data into the dynamic attenuation factor determination model to obtain the driving style attenuation factor.

[0055] Specifically, when determining multi-dimensional dynamic attenuation factors, separate input processing can be performed. Real-time environmental parameters can be input separately into the dynamic attenuation factor determination model, and the model can output a temperature attenuation factor accordingly. Navigation path association information can be input separately into the model, and the model can output the corresponding road condition attenuation factor. User historical driving behavior data can be input separately into the model, and the model can output a matching driving style attenuation factor.

[0056] In some possible implementations, the step of weighting and correcting the theoretical maximum driving range based on the multi-dimensional dynamic attenuation factor to obtain a conservative estimated driving range includes: sequentially multiplying the theoretical maximum driving range with the temperature attenuation factor, the road condition attenuation factor, and the driving style attenuation factor; and using the result of the multiplication operation as the conservative estimated driving range.

[0057] Specifically, the theoretical maximum driving range can be multiplied sequentially by the temperature attenuation factor, road condition attenuation factor, and driving style attenuation factor. The result of this multiplication is used as the conservative estimated driving range. This correction calculation method can combine multi-dimensional attenuation factors to obtain the vehicle's conservative estimated driving range, allowing users to know the minimum guaranteed value of the vehicle's driving range.

[0058] In some possible implementations, constructing a scenario-based driving range by combining the theoretical maximum driving range includes: using the conservatively estimated driving range as the lower limit of the scenario-based driving range and the theoretical maximum driving range as the upper limit of the scenario-based driving range; defining the scenario-based driving range that includes a range of driving uncertainty through the lower limit and the upper limit.

[0059] Among them, the range uncertainty range can be understood as the range in which the actual range of the vehicle may be, which is between the conservatively estimated range and the theoretical maximum range.

[0060] Specifically, the conservatively estimated driving range obtained after correction by multi-dimensional attenuation factors is used as the lower limit of the scenario-based driving range, and the theoretical maximum driving range calculated based on ideal conditions is used as the upper limit of this range. Through this lower limit and upper limit, the scenario-based driving range including the range uncertainty interval can be defined. This allows users to clearly know the fluctuation range of the vehicle's driving range, which is convenient for trip planning.

[0061] In some possible implementations, visualizing the scenario-based driving range range includes: generating and rendering a dynamic driving range ring on the human-machine interface, wherein the inner ring of the dynamic driving range ring corresponds to the conservatively estimated driving range, the outer ring corresponds to the theoretical maximum driving range, and the annular area of ​​the dynamic driving range ring uses a color gradient to represent the uncertainty of the driving range; generating and displaying scenario cards on the human-machine interface, wherein the scenario cards contain a description of a preset driving scenario and the estimated driving range under the corresponding scenario; and, when the user sets a navigation destination, calculating the estimated remaining battery power of the vehicle after reaching the navigation destination based on the scenario-based driving range range, and displaying the estimated remaining battery power of the vehicle on the human-machine interface.

[0062] Among them, the human-machine interface can be a display interface in the vehicle or supporting terminal that enables users to interact with the vehicle system information; the dynamic range ring is a visual element that displays the vehicle's range in the form of a ring graphic; the scenario card is a visual card that displays the vehicle's estimated range under different driving scenarios; and the preset driving scenario can be a pre-set vehicle driving scenario with typical characteristics.

[0063] Specifically, a dynamic range ring can be generated and rendered in the human-machine interface. The conservatively estimated range is mapped to the inner ring of the dynamic range ring, and the theoretical maximum range is mapped to the outer ring. A color gradient is used in the annular area of ​​the dynamic range ring to represent the uncertainty of the range, allowing users to intuitively perceive changes in range risk. At the same time, scenario cards are generated and displayed in the human-machine interface. The cards contain descriptions of preset driving scenarios and the estimated range in the corresponding scenarios, providing a reference for trip planning in different scenarios. In addition, when the user sets a navigation destination, the estimated remaining battery power of the vehicle after reaching the navigation destination is calculated based on the scenario-based range range range, and the estimated remaining battery power is displayed in the human-machine interface.

[0064] By visualizing the output, abstract battery life data is transformed into intuitive graphics and text information, lowering the barrier to information interpretation for users. The estimated remaining battery power upon arrival at the destination can also provide direct basis for users' charging decisions and trip planning.

[0065] The technical solution of this invention obtains real-time vehicle status parameters, real-time environmental parameters, and navigation path association information, and calculates the vehicle's theoretical maximum range based on the real-time status parameters. It then inputs the real-time environmental parameters, navigation path association information, and user historical driving behavior data into a dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor. Based on the multi-dimensional dynamic attenuation factor, a weighted correction is applied to the theoretical maximum range to obtain a conservatively estimated range. This is combined with the theoretical maximum range to construct a scenario-based range range. The scenario-based range range is then visualized and output. This solves the problems of traditional electric vehicle range prediction, which only outputs a single value, is disconnected from actual driving scenarios, has low prediction accuracy, and lacks intuitive range information delivery, leading to significant user range anxiety and difficulty in accurately planning trips. The solution achieves accurate scenario-based range prediction through multi-factor fusion, outputting a range range that closely matches reality. Furthermore, the visualization format lowers the information interpretation threshold, effectively alleviating user range anxiety and providing accurate and intuitive reference for user trip planning and charging decisions.

[0066] Figure 2 This is an architecture diagram of a scenario-based electric vehicle range prediction system provided by an embodiment of the present invention. This embodiment further optimizes the aforementioned embodiments, and its specific implementation can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 2 As shown, the system specifically includes the following modules:

[0067] This system consists of three main modules:

[0068] Data perception layer: Real-time collection of vehicle data (SOC, SOH, real-time energy consumption), environmental data (external temperature, wind speed, precipitation probability), and trip data (navigation-based route, real-time traffic conditions, altitude gradient, historical average vehicle speed).

[0069] Intelligent Prediction Engine (Core Module): Receives all data, runs the prediction algorithm of this invention, and outputs the scenario-based battery range.

[0070] Interactive presentation layer: On the in-vehicle screen or mobile app, the prediction results are presented to the user in the form of map overlays and scene cards.

[0071] like Figure 3 The diagram shown is a flowchart of another scenario-based electric vehicle range prediction method provided by an embodiment of the present invention. The key steps of this scenario-based electric vehicle range prediction method are as follows:

[0072] (1) Basic energy consumption benchmark calculation: First, based on the actual available power of the battery pack. and the vehicle's baseline energy consumption under ideal conditions (Wh / km, watt-hours per kilometer), calculate a theoretical maximum range .

[0073]

[0074] (2) Multidimensional attenuation factor ( Modeling: A series of dynamic attenuation coefficients are introduced to weight and correct the base range. These coefficients are derived by training a machine learning model on massive amounts of real-vehicle data.

[0075] 1) Temperature decay factor ( ):

[0076]

[0077] in, The ambient temperature is used. This function simulates the decrease in battery activity and the significant increase in energy consumption caused by air conditioning heating at low temperatures.

[0078] 2) Road condition attenuation factor ( ):

[0079]

[0080] This factor takes into account all aspects. Real-time congestion level cumulative elevation gain along the route and Road type (highway / city).

[0081] 3) Driving style attenuation factor ( ):

[0082]

[0083] Based on the user's historical driving behavior (such as rapid acceleration) Emergency braking frequency ) Conduct personalized assessments.

[0084] (3) Calculation of overall scenario-based battery range: The final scenario-based battery range is not a fixed value, but a range. The calculation method is as follows:

[0085]

[0086] Remain unchanged, as a theoretical reference. This represents the "conservatively estimated range" under the worst-case (but most likely) overall scenario.

[0087] Visualization solution:

[0088] To achieve the beneficial effects of this invention, this embodiment features a completely new visual interface. The main system interface presents the results in the form of a dynamic battery life ring. Users can clearly see at a glance that they have a reliable "inner ring" of battery life (…). ) and a theoretical "outer ring" range ( The annular region between the two is represented by a color gradient to indicate the range of uncertainty in battery life.

[0089] At the same time, the system provides:

[0090] Scenario cards: Directly tell users the estimated range in kilometers under "winter city commuting" or "highway long-distance" modes.

[0091] Trip simulation: After the user sets the navigation, it directly displays "estimated remaining battery power after arriving at the destination", providing the user with the most direct basis for decision-making.

[0092] Compared with existing technologies, this invention brings about improved user experience and technological advancement:

[0093] 1. Significantly alleviates range anxiety: By providing a "reliable range" rather than a single, misleading figure, it fundamentally eliminates users' guesswork and distrust. Users' concerns about the inner ring... Having a high level of confidence allows you to plan your trip with peace of mind.

[0094] 2. Precise decision support: The "remaining battery power upon arrival" prediction function allows users to know clearly before setting off whether they can reach their destination, whether they need to charge along the way, and when is the best time to charge, realizing a shift from "anxiety" to "planning".

[0095] 3. Personalization and Adaptation: The model fully considers individual driving styles and real-time external environment, making the prediction results highly accurate for specific users and specific trips, far exceeding traditional algorithms based on average values.

[0096] 4. Intuitive and efficient information delivery: Through visual designs such as "dynamic battery life ring" and "scenario cards", complex technical parameters are transformed into a clear visual language, which greatly reduces the cognitive load on users and makes information delivery more user-friendly and efficient.

[0097] Figure 4 This is a schematic diagram of a scenario-based electric vehicle range prediction device provided in an embodiment of the present invention. Figure 4 As shown, the device includes:

[0098] The parameter acquisition and mileage calculation module 410 is used to acquire the vehicle's real-time status parameters, real-time environmental parameters and navigation path association information, and calculate the vehicle's theoretical maximum range based on the real-time status parameters.

[0099] The attenuation factor acquisition module 420 is used to input the real-time environmental parameters, the navigation path association information and the user's historical driving behavior data into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor.

[0100] The range construction module 430 is used to perform weighted correction on the theoretical maximum range based on the multi-dimensional dynamic attenuation factor to obtain a conservative estimated range, and to construct a scenario-based range based on the theoretical maximum range.

[0101] The visualization output module 440 is used to visualize the scenario-based driving range.

[0102] In some possible implementations, the parameter acquisition and mileage calculation module 410 includes:

[0103] The vehicle parameter acquisition unit is used to collect the vehicle's remaining battery power, battery health status, and real-time energy consumption data as real-time status parameters of the vehicle.

[0104] An environmental parameter acquisition unit is used to collect external temperature, wind speed, and precipitation probability data as real-time environmental parameters.

[0105] The path parameter acquisition unit is used to collect navigation-based path information, real-time traffic conditions, altitude gradient and historical average vehicle speed data as navigation path association information;

[0106] The parameter extraction and calculation unit is used to extract the remaining battery power and battery health status from the real-time status parameters, calculate the actual usable power of the battery pack, and extract the benchmark energy consumption under ideal vehicle conditions from the real-time status parameters.

[0107] The theoretical mileage calculation unit is used to calculate the theoretical maximum driving range of the vehicle based on the ratio of the actual available power of the battery pack to the benchmark energy consumption.

[0108] In some possible implementations, the dynamic attenuation factor determination model is a machine learning model pre-trained based on massive amounts of real vehicle data, and the multi-dimensional dynamic attenuation factor includes a temperature attenuation factor, a road condition attenuation factor, and a driving style attenuation factor.

[0109] In some possible implementations, the attenuation factor acquisition module 420 includes:

[0110] A temperature factor acquisition unit is used to input the real-time environmental parameters into the dynamic attenuation factor determination model to obtain the temperature attenuation factor.

[0111] A road condition factor acquisition unit is used to input the navigation path association information into the dynamic attenuation factor determination model to obtain the road condition attenuation factor.

[0112] The driving style factor acquisition unit is used to input the user's historical driving behavior data into the dynamic attenuation factor determination model to obtain the driving style attenuation factor.

[0113] In some possible implementations, the range construction module 430 includes:

[0114] The range correction unit is used to perform a product operation on the theoretical maximum range, the temperature attenuation factor, the road condition attenuation factor, and the driving style attenuation factor in sequence, and use the result of the product operation as the conservative estimated range.

[0115] The range definition unit is used to define the scenario-based driving range range by taking the conservatively estimated driving range as the lower limit of the scenario-based driving range range and the theoretical maximum driving range as the upper limit of the scenario-based driving range range, thereby defining the scenario-based driving range range that includes the range uncertainty interval through the lower limit and the upper limit.

[0116] In some possible implementations, the visualization output module 440 includes:

[0117] The battery life ring rendering unit is used to generate and render a dynamic battery life ring in the human-computer interaction interface. The inner ring of the dynamic battery life ring corresponds to the conservatively estimated battery life, and the outer ring corresponds to the theoretical maximum battery life. The ring-shaped area of ​​the dynamic battery life ring uses a color gradient to represent the uncertainty of the battery life.

[0118] A scenario card generation unit is used to generate and display scenario cards on the human-computer interaction interface. The scenario cards include a preset driving scenario description and the estimated driving range under the corresponding scenario.

[0119] The remaining battery power calculation and display unit is used to calculate the estimated remaining battery power of the vehicle after reaching the navigation destination based on the scenario-based driving range, and to display the estimated remaining battery power of the vehicle on the human-machine interface, when the user sets a navigation destination.

[0120] The scenario-based electric vehicle range prediction device provided in this embodiment of the invention can execute the scenario-based electric vehicle range prediction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0121] Figure 5This is a schematic diagram of the structure of an electronic device for implementing the scenario-based electric vehicle range prediction method of this invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0122] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0123] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0124] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as method XXX.

[0125] In some embodiments, the scenario-based electric vehicle range prediction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the scenario-based electric vehicle range prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the scenario-based electric vehicle range prediction method by any other suitable means (e.g., by means of firmware).

[0126] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0128] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0129] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0130] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0131] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0132] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0133] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A scenario-based method for predicting the driving range of electric vehicles, characterized in that, include: The vehicle's real-time status parameters, environmental real-time parameters, and navigation path association information are obtained, and the vehicle's theoretical maximum range is calculated based on the real-time status parameters. The real-time environmental parameters, the navigation path association information, and the user's historical driving behavior data are input into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor. Based on the multi-dimensional dynamic attenuation factor, the theoretical maximum range is weighted and corrected to obtain a conservative estimated range, and a scenario-based range is constructed in combination with the theoretical maximum range. The scenario-based driving range will be visualized and output.

2. The method according to claim 1, characterized in that, The process of acquiring real-time vehicle status parameters, real-time environmental parameters, and navigation path association information, and calculating the vehicle's theoretical maximum range based on the real-time status parameters, includes: Collect the vehicle's remaining battery power, battery health status, and real-time energy consumption data as real-time status parameters of the vehicle. External temperature, wind speed, and precipitation probability data are collected as real-time environmental parameters. Collect navigation-based route information, real-time traffic conditions, altitude gradient, and historical average vehicle speed data as the navigation route association information; The remaining battery power and battery health status are extracted from the real-time status parameters to calculate the actual usable battery power and the baseline energy consumption under ideal vehicle conditions are extracted from the real-time status parameters. The theoretical maximum driving range of the vehicle is calculated based on the ratio of the actual available power of the battery pack to the baseline energy consumption.

3. The method according to claim 1, characterized in that, The dynamic attenuation factor determination model is a machine learning model pre-trained based on massive amounts of real vehicle data. The multi-dimensional dynamic attenuation factor includes temperature attenuation factor, road condition attenuation factor, and driving style attenuation factor.

4. The method according to claim 3, characterized in that, The process involves inputting the real-time environmental parameters, navigation path association information, and user historical driving behavior data into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor, including: The real-time environmental parameters are input into the dynamic attenuation factor determination model to obtain the temperature attenuation factor; The navigation path association information is input into the dynamic attenuation factor determination model to obtain the road condition attenuation factor; The user's historical driving behavior data is input into the dynamic attenuation factor determination model to obtain the driving style attenuation factor.

5. The method according to claim 3, characterized in that, The process of weighting and correcting the theoretical maximum driving range based on the multi-dimensional dynamic attenuation factor to obtain a conservative estimated driving range includes: The theoretical maximum driving range is then multiplied sequentially by the temperature attenuation factor, the road condition attenuation factor, and the driving style attenuation factor. The result of the product operation is used as the conservatively estimated driving range.

6. The method according to claim 1, characterized in that, The construction of a scenario-based driving range based on the theoretical maximum driving range includes: The conservatively estimated driving range is used as the lower limit of the scenario-based driving range, and the theoretical maximum driving range is used as the upper limit of the scenario-based driving range. The lower limit and the upper limit define the scenario-based driving range that includes the range uncertainty interval.

7. The method according to claim 1, characterized in that, The step of visualizing the scenario-based driving range includes: A dynamic range ring is generated and rendered in the human-computer interaction interface. The inner ring of the dynamic range ring corresponds to the conservatively estimated range, and the outer ring corresponds to the theoretical maximum range. The ring-shaped area of ​​the dynamic range ring uses a color gradient to represent the uncertainty of the range. A scenario card is generated and displayed on the human-computer interaction interface. The scenario card includes a description of a preset driving scenario and the estimated driving range under the corresponding scenario. When the user sets a navigation destination, the estimated remaining battery power of the vehicle after reaching the navigation destination is calculated based on the scenario-based driving range, and the estimated remaining battery power of the vehicle is displayed on the human-computer interaction interface.

8. A scenario-based electric vehicle range prediction device, characterized in that, include: The parameter acquisition and mileage calculation module is used to acquire the vehicle's real-time status parameters, real-time environmental parameters, and navigation path association information, and calculate the vehicle's theoretical maximum range based on the real-time status parameters. The attenuation factor acquisition module is used to input the real-time environmental parameters, the navigation path association information and the user's historical driving behavior data into the dynamic attenuation factor determination model to obtain a multi-dimensional dynamic attenuation factor. The range construction module is used to perform weighted correction on the theoretical maximum range based on the multi-dimensional dynamic attenuation factor to obtain a conservative estimated range, and to construct a scenario-based range based on the theoretical maximum range. The visualization output module is used to visualize the scenario-based driving range.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the scenario-based electric vehicle range prediction method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the scenario-based electric vehicle range prediction method according to any one of claims 1-7.

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