A method, apparatus, electronic device, and storage medium for predicting pure electric driving range

Abstract

This application relates to the field of pure electric range prediction technology, and provides a method, device, electronic device, and storage medium for pure electric range prediction. The method obtains the real-time remaining battery power of the current vehicle and its current driving path; determines the target vehicle based on the current vehicle's driving parameters and the real-time road conditions of the current driving path; determines the historical energy consumption per unit mileage of the target vehicle on the target driving path, and determines the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage, with the road conditions of the target driving path and the real-time road conditions of the current driving path being the same; and predicts the pure electric range of the current vehicle based on the real-time remaining battery power and the predicted energy consumption per unit mileage, achieving the effect of accurately obtaining the predicted energy consumption per unit mileage, and thus achieving the effect of accurately obtaining the predicted range based on the predicted energy consumption per unit mileage and the real-time remaining battery power, avoiding the problem that pure electric vehicles cannot accurately obtain the mileage corresponding to the remaining battery power.

Description

Technical Field

[0001] This application relates to the field of pure electric range prediction technology, and in particular to a pure electric range prediction method, device, electronic device and storage medium. Background Technology

[0002] New energy vehicles refer to automobiles that use unconventional vehicle fuels as their power source (or use conventional vehicle fuels and adopt new on-board power devices), and integrate advanced technologies in vehicle power control and drive, resulting in vehicles with advanced technical principles, new technologies, and new structures.

[0003] Battery electric vehicles (BEVs) are a type of new energy vehicle that uses batteries as an energy storage power source. The batteries provide electrical energy to the electric motor, which in turn drives the vehicle. BEVs have gained widespread acceptance and are rapidly becoming more common due to their advantages such as zero emissions, high energy efficiency, simple structure, low noise, and readily available materials. However, in daily use, BEVs often cannot accurately provide real-time range information, leading to range anxiety for users. Summary of the Invention

[0004] In view of this, embodiments of this application provide a pure electric range prediction method, device, electronic device, and storage medium to solve the problem in the prior art that pure electric vehicles cannot accurately obtain the range corresponding to the remaining battery power, resulting in range anxiety for users.

[0005] A first aspect of this application provides a pure electric range prediction method, the method comprising: obtaining the real-time remaining battery power of a current vehicle and obtaining the current driving path of the current vehicle; determining a target vehicle based on the driving parameters of the current vehicle and the real-time road conditions of the current driving path; determining the historical energy consumption per unit mileage of the target vehicle on the target driving path, and determining the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage, wherein the road conditions of the target driving path and the real-time road conditions of the current driving path are the same; and predicting the pure electric range of the current vehicle based on the real-time remaining battery power and the predicted energy consumption per unit mileage.

[0006] A second aspect of this application provides a pure electric range prediction device, comprising: an acquisition module for acquiring the real-time remaining battery power of a current vehicle and acquiring the current driving path of the current vehicle; a determination module for determining a target vehicle based on the driving parameters of the current vehicle and the real-time road conditions of the current driving path; the determination module is further configured to determine the historical energy consumption per unit mileage of the target vehicle on the target driving path, and determine the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage; and a prediction module for predicting the pure electric range of the current vehicle based on the real-time remaining battery power and the predicted energy consumption per unit mileage.

[0007] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0008] A fourth aspect of this application provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0009] The beneficial effects of this application embodiment compared with the prior art are as follows: In the example of this application, the real-time remaining battery power of the current vehicle and the current driving path of the current vehicle are obtained; the target vehicle is determined based on the driving parameters of the current vehicle and the real-time road conditions of the current driving path; the historical energy consumption per unit mileage of the target vehicle on the target driving path is determined, and the predicted energy consumption per unit mileage of the current vehicle is determined based on the historical energy consumption per unit mileage, and the road conditions of the target driving path and the real-time road conditions of the current driving path are the same; based on the real-time remaining battery power and the predicted energy consumption per unit mileage, the pure electric range of the current vehicle is predicted. This example achieves the effect of accurately obtaining the predicted energy consumption per unit mileage by determining the accurate historical energy consumption per unit mileage of the target vehicle on the target driving path, and then determining the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage. This achieves the effect of accurately obtaining the predicted range based on the predicted energy consumption per unit mileage and the real-time remaining battery power, avoiding the problem of range anxiety caused by the inability of pure electric vehicles to accurately obtain the range corresponding to the remaining battery power in related technologies. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1This is a flowchart illustrating a pure electric mileage prediction method provided in an embodiment of this application;

[0012] Figure 2 This is a flowchart illustrating another pure electric range prediction method provided in an embodiment of this application;

[0013] Figure 3 This is a flowchart illustrating another pure electric range prediction method provided in the embodiments of this application;

[0014] Figure 4 This is a flowchart illustrating another pure electric mileage prediction method provided in the embodiments of this application;

[0015] Figure 5 This is a flowchart illustrating another optional pure electric range prediction method provided in the embodiments of this application;

[0016] Figure 6 This is a flowchart illustrating another optional pure electric range prediction method provided in the embodiments of this application;

[0017] Figure 7 This is a flowchart illustrating another optional pure electric range prediction method provided in the embodiments of this application;

[0018] Figure 8 This is a schematic diagram of the structure of a pure electric mileage prediction device provided in an embodiment of this application;

[0019] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] A pure electric mileage prediction method and apparatus according to embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0022] Figure 1 This application provides a pure electric range prediction method, such as... Figure 1 As shown, the method includes:

[0023] S101. Obtain the real-time remaining battery power of the current vehicle and obtain the current driving route of the current vehicle;

[0024] S102. Determine the target vehicle based on the current vehicle's driving parameters and the real-time road conditions of the current driving route;

[0025] S103. Determine the historical energy consumption per unit mileage of the target vehicle on the target driving route, and determine the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage. The road conditions of the target driving route and the real-time road conditions of the current driving route are the same.

[0026] S104. Based on the real-time remaining battery power and the predicted energy consumption per unit mileage, predict the pure electric range of the current vehicle.

[0027] Specifically, the pure electric range prediction method provided in this example is applied to pure electric vehicles, which include vehicles with autonomous or intelligent driving capabilities (including passenger vehicles (such as cars, buses, coaches, minibuses, etc.), cargo vehicles (such as ordinary trucks, box trucks, trailer trucks, enclosed trucks, tank trucks, flatbed trucks, container trucks, dump trucks, special structure trucks), special vehicles (such as logistics delivery vehicles, automated guided vehicles (AGVs), patrol vehicles, cranes, excavators, bulldozers, loaders, road rollers, off-road engineering vehicles, armored engineering vehicles, sewage treatment vehicles, sanitation vehicles, vacuum trucks, floor scrubbers, water sprinkler trucks, sweeping robots, food delivery robots, shopping guide robots, lawnmowers, golf carts, etc.), vehicles with entertainment functions (such as amusement vehicles, amusement park autonomous driving devices, balance bikes, etc.), and rescue vehicles (such as fire trucks, ambulances, power repair vehicles, engineering emergency rescue vehicles, etc.)).

[0028] In some examples, methods for obtaining the real-time remaining battery power of a vehicle include, but are not limited to, the open-circuit voltage method, the ampere-hour integration method, and the Kalman filtering method. Specifically, for example, obtaining the real-time remaining battery power of a vehicle based on the ampere-hour integration method includes estimating the real-time remaining battery power by accumulating the amount of electricity charged and discharged during the charging and discharging of the vehicle's battery.

[0029] It is understood that the current driving path is the road that the vehicle is currently traveling on; obtaining the current driving path of the vehicle includes, but is not limited to, at least one of the following: obtaining the current driving path of the vehicle based on the navigation map; obtaining the current driving path of the vehicle based on the driving direction and the current location.

[0030] In some examples, after obtaining the current vehicle's current driving path, the target vehicle is determined based on the current vehicle's driving parameters and the real-time traffic conditions along the current driving path. It is understood that the target vehicle's driving parameters are similar to those of the current vehicle, and the target vehicle is one that has traveled along the target driving path. The specific method for determining the target vehicle will be explained in detail later and will not be repeated here.

[0031] In some examples, after identifying the target vehicle, the historical energy consumption per unit mileage of the target vehicle on the target driving path is determined, and the predicted energy consumption per unit mileage of the current vehicle is determined based on the historical energy consumption per unit mileage. The road conditions of the target driving path are the same as the real-time road conditions of the current driving path. Specifically, the target driving path can be the current driving path or other paths with the same real-time road conditions as the current driving path. Specifically, the real-time road conditions of the current driving path are determined by acquiring parameters such as traffic density, average traffic speed, congestion, road conditions, weather conditions, accidents, and construction on the current driving path. After determining the real-time road conditions of the current driving path, the historical road conditions of any path are acquired. When the historical road conditions of any path are the same as or similar to the real-time road conditions of the current driving path, the path with the same or similar road conditions is taken as the target driving path.

[0032] Following the previous example, after determining the target driving route, this example determines the accurate historical energy consumption per unit mileage of the target vehicle on the target driving route, and then determines the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage. This achieves the effect of accurately obtaining the predicted energy consumption per unit mileage, avoiding the problem in related technologies where the predicted energy consumption per unit mileage cannot be accurately obtained.

[0033] In some examples, after obtaining the predicted energy consumption per unit mile, this example predicts the pure electric range of the current vehicle based on the real-time remaining battery power and the predicted energy consumption per unit mile. The specific method for predicting the pure electric range of the current vehicle based on the real-time remaining battery power and the predicted energy consumption per unit mile will be explained in detail later, and will not be repeated here.

[0034] According to the technical solution provided in this application embodiment, the real-time remaining battery power of the current vehicle and the current driving path of the current vehicle are obtained; a target vehicle is determined based on the driving parameters of the current vehicle and the real-time road conditions of the current driving path; the historical energy consumption per unit mileage of the target vehicle on the target driving path is determined, and the predicted energy consumption per unit mileage of the current vehicle is determined based on the historical energy consumption per unit mileage, and the road conditions of the target driving path and the real-time road conditions of the current driving path are the same; based on the real-time remaining battery power and the predicted energy consumption per unit mileage, the pure electric range of the current vehicle is predicted. This example achieves the effect of accurately obtaining the predicted energy consumption per unit mileage by determining the accurate historical energy consumption per unit mileage of the target vehicle on the target driving path, and then determining the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage. This achieves the effect of accurately obtaining the predicted range based on the predicted energy consumption per unit mileage and the real-time remaining battery power, avoiding the problem of range anxiety caused by the inability of pure electric vehicles to accurately obtain the range corresponding to the remaining battery power in related technologies.

[0035] In some embodiments, such as Figure 2As shown, based on the current vehicle's driving parameters and the real-time traffic conditions of the current driving route, the target vehicle is determined, including:

[0036] S201. Based on the current vehicle's driving parameters, determine historically similar vehicles. Historically similar vehicles are those whose driving parameters are more similar to the current vehicle than a target threshold.

[0037] S202. Determine the target driving route based on the real-time traffic conditions of the current driving route;

[0038] S203. Based on historical similar vehicles and target driving routes, determine the target vehicle.

[0039] Specifically, the energy consumption per unit distance of a vehicle is related to driving speed, temperature, energy consumption, and road conditions. Since road conditions are related to the current driving route but not to the vehicle itself, this example obtains the current vehicle's driving speed, temperature, and energy consumption as the current vehicle's driving parameters.

[0040] In some examples, after obtaining the driving parameters of the current vehicle, the driving parameters of multiple historical vehicles are obtained. These historical vehicle driving parameters are then compared with the current vehicle's driving parameters. Historical vehicles whose driving parameter similarity exceeds a target threshold are considered historically similar vehicles. It is understood that the specific methods for determining the similarity between historical parameters and the current vehicle's driving parameters include, but are not limited to, cosine similarity calculation, Euclidean distance calculation, and Manhattan distance calculation. Preferably, this example uses cosine similarity calculation to calculate the similarity between the driving parameters of historical vehicles and the current vehicle.

[0041] Specifically, for example, firstly, the driving parameters of the current vehicle are determined using onboard sensors, GPS devices, etc., and then the driving parameters of the current vehicle are normalized to obtain a first comparison parameter (wherein, data normalization is performed to scale different types of data to the same scale range; this embodiment does not limit the method of data normalization, for example, Z-score normalization or minimum-maximum scaling can be used for data normalization); then, the driving parameters of historical vehicles are obtained, and then the driving parameters of historical vehicles are normalized to obtain a second comparison parameter; then, the cosine similarity of the first comparison parameter and the second comparison parameter is calculated to obtain the similarity of the driving parameters between the historical vehicle and the current vehicle; finally, the calculated similarity is compared with a target threshold. If the similarity between the driving parameters of the historical vehicle and the driving parameters of the current vehicle exceeds the target threshold, then the historical vehicle is considered a historically similar vehicle; otherwise, if the similarity between the driving parameters of the historical vehicle and the driving parameters of the current vehicle does not exceed the target threshold, then the historical vehicle is not considered a historically similar vehicle.

[0042] It is understandable that the aforementioned historical vehicles are vehicles with accurate energy consumption per unit mileage. The driving parameters of the historical vehicles are stored in a database, and the current vehicle can read data from the database to obtain the driving parameters of the historical vehicles.

[0043] In some examples, a target driving route is determined based on the real-time traffic conditions of the current driving route. This target driving route is a historical driving route. Specifically, the real-time traffic conditions of the current driving route are determined by acquiring parameters such as traffic density, average traffic speed, congestion, road conditions, weather conditions, accidents, and construction. After determining the real-time traffic conditions of the current driving route, the historical traffic conditions of any route are acquired. If the historical traffic conditions of any route are the same as or similar to the real-time traffic conditions of the current driving route, then the route with the same or similar traffic conditions is taken as the target driving route.

[0044] Continuing the previous example, for instance, there are multiple driving paths A, B...N. Driving path A is the current driving path of the current vehicle, and the real-time traffic conditions of driving path A are denoted as X (X includes parameters such as driving path length, number of curves, weather, temperature, congestion, and road conditions). Then, the historical traffic conditions of driving paths A to N before the current time are obtained one by one. If the historical traffic conditions of any driving path from A to N before the current time are close to X, then the driving path from A to N that is close to the real-time traffic conditions X of driving path A is selected as the target driving path.

[0045] Finally, after identifying historically similar vehicles and target driving routes, the target vehicle is determined based on these historically similar vehicles and target driving routes. This achieves accurate identification of the target vehicle, and the target vehicle's driving parameters are similar to or the same as the current vehicle's driving parameters. Furthermore, the target driving route corresponding to the target vehicle has the same road conditions as the current driving route. Therefore, determining the historical unit mileage of the target vehicle on the target driving route can have higher reference value, avoiding the problem of randomly obtaining historical unit mileage energy consumption, which leads to a large discrepancy with the current vehicle's actual unit mileage energy consumption.

[0046] According to the technical solution provided in the embodiments of this application, historically similar vehicles are determined based on the current vehicle's driving parameters; the target driving path is determined based on the real-time road conditions of the current driving path, thus achieving accurate acquisition of the target driving path; the target vehicle is determined based on historically similar vehicles and the target driving path. After determining historically similar vehicles and the target driving path, the target vehicle is determined based on historically similar vehicles and the target driving path, thus achieving accurate determination of the target vehicle. Furthermore, the driving parameters of the target vehicle are similar to or the same as those of the current vehicle, and the road conditions of the target driving path corresponding to the target vehicle are the same as those of the current driving path. Therefore, determining the historical unit mileage of the target vehicle on the target driving path can have higher reference value, avoiding the problem of randomly obtaining historical unit mileage energy consumption, which cannot accurately obtain the predicted unit mileage energy consumption.

[0047] In some embodiments, such as Figure 3 As shown, based on historically similar vehicles and the target's travel path, the target vehicle is determined, including:

[0048] S301. Determine whether there is a correlation between historically similar vehicles and the target travel path;

[0049] S302. If there is a correlation between historically similar vehicles and the target driving path, then the historically similar vehicles will be used as the target vehicles.

[0050] Specifically, it is determined whether there is a correlation between historically similar vehicles and the target driving route. If there is a correlation, the historically similar vehicles will be used as the target vehicles; otherwise, if there is no correlation, the historically similar vehicles will not be used as the target vehicles.

[0051] In some examples, determining whether there is a correlation between historically similar vehicles and the target driving path includes, but is not limited to: determining whether historically similar vehicles have traveled on the target driving path; if historically similar vehicles have traveled on the target driving path, then it is determined that there is a correlation between historically similar vehicles and the target driving path; otherwise, if historically similar vehicles have not traveled on the target driving path, then it is determined that there is no correlation between historically similar vehicles and the target driving path.

[0052] In some examples, if a historically similar vehicle has traveled on the target route and the road conditions during its travel are the same as the real-time road conditions of the current route, then it is determined that there is a correlation between the historically similar vehicle and the target route; conversely, if a historically similar vehicle has not traveled on the target route, then it is determined that there is no correlation between the historically similar vehicle and the target route; or if a historically similar vehicle has traveled on the target route and the road conditions during its travel on the target route are different from the real-time road conditions of the current route, then it is determined that there is no correlation between the historically similar vehicle and the target route.

[0053] It is understandable that the specific method for determining whether there is a correlation between historically similar vehicles and the target travel path can be flexibly set by relevant personnel according to actual needs.

[0054] According to the technical solution provided in the embodiments of this application, it is determined whether there is a correlation between historically similar vehicles and the target driving path; if there is a correlation between historically similar vehicles and the target driving path, then the historically similar vehicles are used as the target vehicles, thus achieving accurate determination of the target vehicles. This avoids the problem in related technologies where randomly obtaining historical unit mileage energy consumption results in a significant difference from the actual unit mileage energy consumption of the current vehicle, and the inability to accurately obtain predicted unit mileage energy consumption based on randomly obtained historical unit mileage energy consumption.

[0055] In some embodiments, such as Figure 4 As shown, the predicted energy consumption per unit mile for the current vehicle is determined based on historical energy consumption per unit mile, including:

[0056] S401. Based on the current vehicle's battery degradation rate, current vehicle's driving parameters, and real-time road conditions of the current driving route, determine the current vehicle's initial energy consumption per unit mileage.

[0057] S402. Determine the weights corresponding to the initial energy consumption per unit mileage and the historical energy consumption per unit mileage, respectively.

[0058] S403. Based on the determined weights, the initial unit mileage energy consumption and the historical unit mileage energy consumption are weighted to obtain the predicted unit mileage energy consumption for the current vehicle.

[0059] Specifically, battery degradation refers to the degree to which battery capacity decreases over time and with use. Newer batteries typically have lower degradation rates, while aging batteries may experience increased energy consumption due to capacity reduction. Therefore, the battery's condition and age need to be considered. Driving parameters include vehicle speed, load, acceleration, braking, etc. These parameters affect the vehicle's current energy consumption; for example, high-speed driving usually requires more energy. Real-time traffic conditions along the current driving route include, but are not limited to, traffic congestion, gradient, and slippery road surfaces. This information affects the vehicle's energy consumption; for example, in congested traffic, the vehicle may need to brake and accelerate frequently, increasing energy consumption. This example achieves accurate initial energy consumption per unit mile by comprehensively considering the vehicle's current battery degradation, driving parameters, and real-time traffic conditions along the current driving route. Specifically, the vehicle's current battery degradation, driving parameters, and real-time traffic conditions are used as input parameters into the initial energy consumption per unit mile calculation model to accurately obtain the initial energy consumption per unit mile.

[0060] It is understandable that the above initial energy consumption calculation model per unit mileage is trained based on training parameters that include "battery degradation, driving parameters, real-time road conditions and mileage data".

[0061] In some examples, after determining the initial energy consumption per unit mile, the weights of the initial energy consumption per unit mile and the historical energy consumption per unit mile are determined. It is understood that the sum of the weights of the initial energy consumption per unit mile and the historical energy consumption per unit mile is 1. For example, if the weight of the initial energy consumption per unit mile is A and the weight of the historical energy consumption per unit mile is B, then A + B equals 1. The specific determination of the weights of the initial energy consumption per unit mile and the historical energy consumption per unit mile can be flexibly set by relevant personnel according to actual needs.

[0062] Continuing with the previous example, in some examples, the weights of the initial energy consumption per unit mile and the historical energy consumption per unit mile can be determined by a weight determination model. Specifically, this weight determination model is trained using weight training data, which includes, but is not limited to, initial energy consumption per unit mile training data and historical energy consumption per unit mile training data. In some examples, this weight training model also needs to comprehensively consider the real-time traffic conditions of the current driving path, the real-time traffic conditions of the target driving path, the driving parameters of the current vehicle, and the driving parameters of the target vehicle. Therefore, when training this weight training model, the weight training data also includes real-time traffic condition training data of the current driving path, real-time traffic condition training data of the target driving path, driving parameter training data of the current vehicle, and driving parameter training data of the target vehicle.

[0063] Finally, after determining the weights corresponding to the initial unit mileage energy consumption and the historical unit mileage energy consumption, the initial unit mileage energy consumption and the historical unit mileage energy consumption are weighted based on the determined weights to obtain the predicted unit mileage energy consumption for the current vehicle. This achieves accurate acquisition of the predicted unit mileage energy consumption for the current vehicle, avoiding the problem in related technologies where the predicted unit mileage energy consumption cannot be accurately obtained, resulting in the inability to accurately predict the vehicle's mileage.

[0064] According to the technical solution provided in the embodiments of this application, the initial energy consumption per unit mileage of the current vehicle is determined based on the battery degradation rate of the current vehicle, the driving parameters of the current vehicle, and the real-time road conditions of the current driving route; the weights corresponding to the initial energy consumption per unit mileage and the historical energy consumption per unit mileage are determined respectively; the initial energy consumption per unit mileage and the historical energy consumption per unit mileage are weighted based on the determined weights to obtain the predicted energy consumption per unit mileage of the current vehicle. This achieves accurate acquisition of the predicted energy consumption per unit mileage of the current vehicle, avoiding the problem in related technologies where the predicted energy consumption per unit mileage cannot be accurately obtained, leading to the inability to accurately predict the mileage of the vehicle in the future.

[0065] In some examples, such as Figure 5 As shown, the initial energy consumption per unit mile and the historical energy consumption per unit mile are weighted based on determined weights to obtain the predicted energy consumption per unit mile for the current vehicle, including:

[0066] S501. The initial unit mileage energy consumption is weighted based on the weights corresponding to the initial unit mileage energy consumption to obtain the first predicted unit mileage energy consumption.

[0067] S502. Based on the weights corresponding to the historical unit mileage energy consumption, the historical unit mileage energy consumption is weighted to obtain the second predicted unit mileage energy consumption.

[0068] S503. Sum the first predicted unit mileage energy consumption and the second predicted unit mileage energy consumption to obtain the predicted unit mileage energy consumption.

[0069] Specifically, the initial unit mileage energy consumption is weighted based on the weights corresponding to the initial unit mileage energy consumption to obtain the first predicted unit mileage energy consumption. The specific calculation formula is as follows:

[0070] M1 = D1 * J;

[0071] Where M1 is the first predicted energy consumption per unit mileage, D1 is the initial energy consumption per unit mileage, and J is the weight corresponding to the initial energy consumption per unit mileage. The above formula enables the accurate acquisition of the first predicted energy consumption per unit mileage.

[0072] In some examples, the historical energy consumption per unit mile is weighted based on the weights corresponding to the historical energy consumption per unit mile to obtain the second predicted energy consumption per unit mile. The specific calculation formula is as follows:

[0073] M2 = D2 * K;

[0074] Where M1 is the second predicted energy consumption per unit mileage, D2 is the historical energy consumption per unit mileage, and K is the weight corresponding to the historical energy consumption per unit mileage. The above formula enables the accurate acquisition of the second predicted energy consumption per unit mileage.

[0075] Finally, the predicted energy consumption per unit mile is obtained by summing the first and second predicted energy consumption per unit mile. The specific calculation method is as follows:

[0076] M = M1 + M2;

[0077] Where M is the predicted energy consumption per unit mileage, M1 is the first predicted energy consumption per unit mileage, and M2 is the second predicted energy consumption per unit mileage.

[0078] To better understand the above method, this example provides a more specific illustration. Taking the initial unit mileage energy consumption as having a weight of 0.5 and the historical unit mileage energy consumption as having a weight of 0.5 as an example, the predicted unit mileage energy consumption M = D1*0.5 + D2*0.5 = 0.5*(D1+D2).

[0079] According to the technical solution provided in the embodiments of this application, the initial unit mileage energy consumption is weighted based on the weight corresponding to the initial unit mileage energy consumption to obtain the first predicted unit mileage energy consumption; the historical unit mileage energy consumption is weighted based on the weight corresponding to the historical unit mileage energy consumption to obtain the second predicted unit mileage energy consumption; the first predicted unit mileage energy consumption and the second predicted unit mileage energy consumption are summed to obtain the predicted unit mileage energy consumption, thereby achieving accurate acquisition of the predicted unit mileage energy consumption and avoiding the problem in related technologies where the predicted unit mileage energy consumption cannot be accurately acquired, leading to the inability to accurately predict the mileage of the vehicle in the future.

[0080] In some examples, such as Figure 6 As shown, based on the real-time remaining battery power and predicted energy consumption per unit mile, the pure electric range of the current vehicle is predicted, including:

[0081] S601, perform quotient processing based on real-time remaining power and predicted energy consumption per unit mileage;

[0082] S602. Use the value obtained from the quotient process as the prediction result of the pure electric range of the current vehicle.

[0083] Specifically, a quotient is calculated based on the real-time remaining battery power and the predicted energy consumption per unit mileage; the value obtained from the quotient calculation is used as the predicted pure electric range for the current vehicle. The specific calculation formula is as follows:

[0084] L = S / M;

[0085] Where L = predicted mileage, S = real-time remaining battery power, and M = predicted energy consumption per unit mileage, it can be understood that the above calculation formula achieves the effect of accurately predicting the pure electric mileage of the current vehicle.

[0086] According to the technical solution provided in the embodiments of this application, a quotient process is performed based on the real-time remaining power and the predicted energy consumption per unit mileage; the value obtained from the quotient process is used as the predicted pure electric mileage of the current vehicle, thereby realizing the accurate acquisition of the predicted energy consumption per unit mileage based on the real-time remaining power and the predicted energy consumption per unit mileage, avoiding the problem in related technologies where the predicted energy consumption per unit mileage cannot be accurately acquired, resulting in the inability to accurately predict the mileage of the vehicle in the future.

[0087] In some examples, such as Figure 7 As shown, the method also includes:

[0088] S701. Determine the predicted pure electric range of the current vehicle;

[0089] S702. If the current pure electric range prediction result indicates that the current vehicle's predicted range is lower than the target range, then the target charging station is determined based on the current driving route.

[0090] S703: Issue a charging reminder based on the target charging station.

[0091] Specifically, the pure electric range prediction result of the current vehicle is determined to obtain the predicted range. Then, the predicted range is compared with the target range. If the pure electric range prediction result of the current vehicle indicates that the predicted range of the current vehicle is lower than the target range, then the target charging station is determined according to the current driving route. The target range is the remaining range in the current driving route. It is understood that the method of determining the target charging station according to the current driving route is not limited in this example. For example, the charging station closest to the current driving route can be used as the target charging station.

[0092] In some examples, after the target charging station is identified, a charging reminder is issued based on the target charging station, so that the user can obtain charging information and adjust the route according to the target charging station in the charging information. This ensures that the current vehicle's charge level after charging can support the complete journey of the current route, further alleviating the user's range anxiety.

[0093] According to the technical solution provided in the embodiments of this application, the pure electric range prediction result of the current vehicle is determined; if the pure electric range prediction result of the current vehicle indicates that the predicted range of the current vehicle is lower than the target range, then the target charging station is determined according to the current driving route; a charging reminder is issued according to the target charging station, thereby realizing charging guidance for users based on accurate predicted range, further alleviating users' range anxiety.

[0094] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0095] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0096] This embodiment also provides a pure electric range prediction device, such as... Figure 8 As shown, the device includes:

[0097] The acquisition module 801 is used to acquire the real-time remaining battery power of the current vehicle and the current driving route of the current vehicle;

[0098] The determination module 802 is used to determine the target vehicle based on the current vehicle's driving parameters and the real-time road conditions of the current driving route;

[0099] The determination module is also used to determine the historical energy consumption per unit mileage of the target vehicle on the target driving path, and to determine the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage.

[0100] The prediction module 803 is used to predict the pure electric range of the current vehicle based on the real-time remaining battery power and the predicted energy consumption per unit mileage.

[0101] In some examples, the determination module 802 is also used to determine historically similar vehicles based on the driving parameters of the current vehicle, where historically similar vehicles are those whose driving parameters are more similar to those of the current vehicle than a target threshold; determine the target driving path based on the real-time traffic conditions of the current driving path; and determine the target vehicle based on the historically similar vehicles and the target driving path.

[0102] In some examples, the determination module 802 is also used to determine whether there is a correlation between historically similar vehicles and the target driving path; if there is a correlation between historically similar vehicles and the target driving path, then the historically similar vehicles are used as the target vehicles.

[0103] In some examples, the determination module 802 is also used to determine the initial energy consumption per unit mileage of the current vehicle based on the battery degradation of the current vehicle, the driving parameters of the current vehicle, and the real-time traffic conditions of the current driving route; determine the weights corresponding to the initial energy consumption per unit mileage and the historical energy consumption per unit mileage respectively; and perform weighted processing on the initial energy consumption per unit mileage and the historical energy consumption per unit mileage based on the determined weights to obtain the predicted energy consumption per unit mileage of the current vehicle.

[0104] In some examples, the determining module 802 is also used to weight the initial unit mileage energy consumption based on the weight corresponding to the initial unit mileage energy consumption to obtain the first predicted unit mileage energy consumption; to weight the historical unit mileage energy consumption based on the weight corresponding to the historical unit mileage energy consumption to obtain the second predicted unit mileage energy consumption; and to sum the first predicted unit mileage energy consumption and the second predicted unit mileage energy consumption to obtain the predicted unit mileage energy consumption.

[0105] In some examples, the prediction module 803 is also used to perform a quotient process based on the real-time remaining battery power and the predicted energy consumption per unit mileage; the value obtained from the quotient process is used as the predicted pure electric mileage of the current vehicle.

[0106] In some examples, the prediction module 803 is also used to determine the pure electric range prediction result of the current vehicle; if the pure electric range prediction result of the current vehicle indicates that the predicted range of the current vehicle is lower than the target range, then the target charging station is determined according to the current driving route; and a charging reminder is issued according to the target charging station.

[0107] According to the technical solution provided in the embodiments of this application, the above-mentioned device in the embodiments of this application obtains the real-time remaining power of the current vehicle and the current driving path of the current vehicle; determines the target vehicle based on the driving parameters of the current vehicle and the real-time road conditions of the current driving path; determines the historical energy consumption per unit mileage of the target vehicle on the target driving path, and determines the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage, wherein the road conditions of the target driving path and the real-time road conditions of the current driving path are the same; and predicts the pure electric range of the current vehicle based on the real-time remaining power and the predicted energy consumption per unit mileage. This example achieves the effect of accurately obtaining the predicted energy consumption per unit mileage by determining the accurate historical energy consumption per unit mileage of the target vehicle on the target driving path, and then determining the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage. This achieves the effect of accurately obtaining the predicted range based on the predicted energy consumption per unit mileage and the real-time remaining power, avoiding the problem of range anxiety caused by the inability of pure electric vehicles to accurately obtain the range corresponding to the remaining power in related technologies.

[0108] Figure 9 This is a schematic diagram of the electronic device 9 provided in an embodiment of this application. Figure 9As shown, the electronic device 9 of this embodiment includes a processor 901, a memory 902, and a computer program 903 stored in the memory 902 and executable on the processor 901. When the processor 901 executes the computer program 903, it implements the steps in the various method embodiments described above. Alternatively, when the processor 901 executes the computer program 903, it implements the functions of each module / unit in the various device embodiments described above.

[0109] Electronic device 9 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 9 may include, but is not limited to, processor 901 and memory 902. Those skilled in the art will understand that... Figure 9 This is merely an example of electronic device 9 and does not constitute a limitation on electronic device 9. It may include more or fewer components than shown, or different components.

[0110] The processor 901 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0111] The memory 902 can be an internal storage unit of the electronic device 9, such as a hard disk or RAM of the electronic device 9. The memory 902 can also be an external storage device of the electronic device 9, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 9. The memory 902 can also include both internal and external storage units of the electronic device 9. The memory 902 is used to store computer programs and other programs and data required by the electronic device.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0113] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in a computer-readable medium can be appropriately added or removed according to regional requirements and patent practice requirements. For example, in some regions, according to regional requirements and patent practice, a computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0114] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method of predicting a pure electric range, characterized in that, The method includes: Obtain the real-time remaining battery power of the current vehicle and obtain the current driving path of the current vehicle, which is the path of the road that the current vehicle is currently traveling on. The target vehicle is determined based on the current vehicle's driving parameters and the real-time traffic conditions of the current driving route; The historical energy consumption per unit mileage of the target vehicle on the target driving path is determined, and the predicted energy consumption per unit mileage of the current vehicle is determined based on the historical energy consumption per unit mileage. The road conditions of the target driving path are the same as the real-time road conditions of the current driving path. The target driving path is determined by acquiring traffic density, average traffic speed, congestion, road conditions, weather conditions, accident and construction parameters of the current driving path, and the real-time road conditions of any path are acquired. When the historical road conditions of any path are the same as the real-time road conditions of the current driving path, the same path is taken as the target driving path. Based on the real-time remaining battery power and the predicted energy consumption per unit mileage, the pure electric range of the current vehicle is predicted.

2. The method according to claim 1, characterized in that, Based on the current vehicle's driving parameters and the real-time traffic conditions of the current driving route, the target vehicle is determined, including: Based on the driving parameters of the current vehicle, determine historically similar vehicles, which are historical vehicles whose driving parameters are more similar to those of the current vehicle than a target threshold. The target vehicle is determined based on the historical similar vehicles and the target travel path.

3. The method according to claim 2, characterized in that, Determining the target vehicle based on the historically similar vehicles and the target travel path includes: Determine whether there is a correlation between the historically similar vehicles and the target travel path; If there is a correlation between the historically similar vehicle and the target driving path, then the historically similar vehicle will be used as the target vehicle.

4. The method according to claim 1, characterized in that, Determining the predicted energy consumption per unit mile for the current vehicle based on the historical energy consumption per unit mile includes: Based on the current vehicle's battery degradation, the current vehicle's driving parameters, and the real-time traffic conditions of the current driving route, the initial energy consumption per unit mileage of the current vehicle is determined. Determine the weights corresponding to the initial energy consumption per unit mileage and the historical energy consumption per unit mileage, respectively; The initial energy consumption per unit mile and the historical energy consumption per unit mile are weighted based on determined weights to obtain the predicted energy consumption per unit mile for the current vehicle.

5. The method according to claim 4, characterized in that, The initial energy consumption per unit mile and the historical energy consumption per unit mile are weighted based on determined weights to obtain the predicted energy consumption per unit mile for the current vehicle, including: The initial unit mileage energy consumption is weighted based on the weights corresponding to the initial unit mileage energy consumption to obtain the first predicted unit mileage energy consumption; The historical unit mileage energy consumption is weighted based on the weights corresponding to the historical unit mileage energy consumption to obtain the second predicted unit mileage energy consumption. The predicted energy consumption per unit mileage is obtained by summing the first predicted energy consumption per unit mileage and the second predicted energy consumption per unit mileage.

6. The method according to claim 1, characterized in that, Based on the real-time remaining battery power and the predicted energy consumption per unit mileage, the pure electric range of the current vehicle is predicted, including: The quotient is calculated based on the real-time remaining power and the predicted energy consumption per unit mileage. The value obtained by the quotient process is used as the predicted pure electric range of the current vehicle.

7. The method according to claim 1, characterized in that, The method further includes: Determine the predicted pure electric range of the current vehicle; If the predicted pure electric range of the current vehicle indicates that the predicted range of the current vehicle is lower than the target range, then the target charging station is determined based on the current driving route. A charging reminder will be issued based on the target charging station.

8. A pure electric range prediction device, characterized in that, The device includes: The acquisition module is used to acquire the real-time remaining battery power of the current vehicle and the current driving path of the current vehicle, wherein the current driving path is the path of the road that the current vehicle is traveling on. The determination module is used to determine the target vehicle based on the driving parameters of the current vehicle and the real-time traffic conditions of the current driving route; The determining module is further configured to determine the historical energy consumption per unit mileage of the target vehicle on the target driving path, and determine the predicted energy consumption per unit mileage of the current vehicle based on the historical energy consumption per unit mileage; the target driving path is determined by acquiring the traffic density, average traffic speed, congestion, road conditions, weather conditions, accidents and construction parameters of the current driving path to jointly determine the real-time traffic conditions of the current driving path, acquiring the historical traffic conditions of any path, and when the historical traffic conditions of any path are the same as or similar to the real-time traffic conditions of the current driving path, the path with the same or similar traffic conditions is taken as the target driving path; The prediction module is used to predict the pure electric range of the current vehicle based on the real-time remaining battery power and the predicted energy consumption per unit mileage.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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