Hybrid vehicle charging reminding method, device, equipment and storage medium

Abstract

The application provides a method, device and equipment for reminding energy charging of a hybrid vehicle, and a storage medium, which can be used in the field of hybrid vehicles. The method comprises the following steps: obtaining a current parking position of a target vehicle when the target vehicle is turned off and parked; obtaining a predicted driving route of the next trip of the target vehicle based on the current parking position and a set of habitual driving routes of the target vehicle; obtaining a predicted power consumption of the predicted driving route based on historical power consumption data of the habitual driving route corresponding to the predicted driving route; obtaining an energy charging preference of the target vehicle in the case that the remaining power of the target vehicle is less than the sum of the predicted power consumption and a preset safety redundancy power; wherein the energy charging preference comprises a preference for charging or a preference for refueling; and issuing corresponding energy charging reminder information according to the energy charging preference. The method of the application achieves the technical effect of more accurately reminding users of energy charging, makes the energy charging reminder of the vehicle more intelligent, meets the use and travel habits of users, and improves the user experience.

Description

Technical Field

[0001] This application relates to the field of hybrid electric vehicles, and more particularly to a charging reminder method, device, equipment, and storage medium for hybrid electric vehicles. Background Technology

[0002] Currently, vehicle charging reminders for users are basically based on a set charging threshold. Users will only be reminded to charge when the remaining battery or fuel level falls below a fixed threshold.

[0003] Electric vehicles and hybrid vehicles are battery-powered and have limited range, which causes significant range anxiety for users. As a result, charging thresholds are often set relatively high, leading to frequent charging and negatively impacting both user experience and battery life.

[0004] Furthermore, since hybrid vehicles have both charging and refueling modes, simply using the remaining battery or fuel level to remind users of charging status is neither accurate nor suitable for users' usage and travel habits. Summary of the Invention

[0005] This application provides a charging reminder method, device, equipment, and storage medium for hybrid electric vehicles to solve the problem of inaccurate charging reminders in existing hybrid electric vehicles.

[0006] According to the first aspect disclosed in this application, a method for providing a charging reminder for a hybrid electric vehicle is provided, comprising:

[0007] Obtain the current parking position of the target vehicle after it has been turned off and is parked.

[0008] Based on the current parking location and the set of habitual routes of the target vehicle, the predicted driving route for the next trip of the target vehicle is obtained; wherein, the set of habitual routes includes multiple habitual driving routes, and the predicted driving route corresponds to one of the habitual driving routes.

[0009] Based on the historical power consumption data of the habitual driving route corresponding to the predicted driving route, the predicted power consumption of the predicted driving route is obtained.

[0010] If the remaining battery power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power, the charging preference of the target vehicle is obtained; wherein, the charging preference includes a preference for charging or a preference for refueling.

[0011] Based on the charging preferences, a corresponding charging reminder message is sent.

[0012] In one feasible implementation, based on the current parking location and the target vehicle's habitual route set, the predicted driving route for the target vehicle's next trip is obtained, including:

[0013] Starting from the current parking location, search for the usual driving routes with the same starting point in the set of usual routes of the target vehicle to obtain a preliminary set of routes;

[0014] The most frequently used route with the highest historical travel probability value in the initial route set is selected as the predicted travel route for the target vehicle's next trip.

[0015] In one feasible implementation, the method for obtaining the set of preferred routes includes:

[0016] Acquire historical driving data of the target vehicle within a preset collection period;

[0017] Using the historical parking locations where the engine was turned off and parked in the historical driving data as nodes, the travel route is obtained from the historical driving data;

[0018] For each travel route, the similarity of the travel route is compared with the historical travel routes in the historical travel route set of the target vehicle, and the number of historical trips of the historical travel routes that meet the preset similarity conditions is incremented by one;

[0019] Historical routes with the same starting point in the set of historical routes are divided into the same subset of historical routes;

[0020] For each historical route in each subset of historical routes, the ratio between the number of historical trips for that historical route and the total number of historical trips in the subset of historical routes is calculated to obtain the historical trip probability value for that historical route.

[0021] Historical travel routes with a probability value greater than a preset probability threshold are added to the set of commonly used routes.

[0022] In one feasible implementation, the similarity of the travel route with the historical travel routes in the historical travel route set of the target vehicle is compared, and the number of historical trips for historical travel routes that meet the similarity criteria is incremented by one, including:

[0023] Obtain the starting point, ending point, and travel information of the travel route; wherein the travel information includes traffic information and vehicle information;

[0024] Search the set of historical driving routes for those with the same start and end points to obtain a set of candidate routes.

[0025] The similarity of the driving information of historical routes in the candidate route set with the driving information of the travel route is compared, and the number of historical trips of historical routes that meet the preset similarity conditions is incremented by one.

[0026] In one feasible implementation, it further includes:

[0027] If there are no historical routes with the same start and end points in the historical route set, or if there are no historical routes in the candidate route set that meet the preset conditions for similarity of driving information, the travel route will be added to the historical route set.

[0028] In one feasible implementation, the predicted power consumption of the predicted driving route is obtained based on historical power consumption data of the habitual driving route corresponding to the predicted driving route, including:

[0029] Obtain the historical power consumption of the habitual driving route corresponding to the predicted driving route;

[0030] The average value of the historical power consumption is obtained as the predicted power consumption for the predicted driving route.

[0031] In one feasible implementation, obtaining the charging preferences of the target vehicle includes:

[0032] Obtain charging preference data and refueling preference data within a preset statistical period; wherein, the charging preference data and the refueling preference data are the number of charging times and the number of refueling times, pure electric driving mileage and hybrid driving mileage, or historical electricity consumption and historical fuel consumption;

[0033] The charging method corresponding to the preference data with the highest proportion or the proportion reaching a preset value in the charging preference data and the refueling preference data is taken as the charging preference of the target vehicle.

[0034] In one feasible implementation, based on the charging preference, a corresponding charging reminder message is issued, including:

[0035] If the charging preference is set to preferred charging, a charging reminder message will be issued.

[0036] If the charging preference is to refuel, the predicted fuel consumption of the target vehicle is obtained based on the predicted driving route and the remaining battery power.

[0037] If the remaining fuel level of the target vehicle is less than the predicted fuel consumption, a refueling reminder will be issued.

[0038] In one feasible implementation, obtaining the predicted fuel consumption of the target vehicle based on the predicted driving route and the remaining battery power includes:

[0039] The predicted driving route and the remaining battery power are input into the fuel consumption prediction model to obtain the predicted fuel consumption.

[0040] The fuel consumption prediction model is obtained through simulation based on the vehicle data of the target vehicle, or through machine learning training based on the historical travel data of the target vehicle.

[0041] In one feasible implementation, after obtaining the current parking position of the target vehicle after it has been turned off and parked, the method further includes:

[0042] If the current parking location is a historical charging location and the current parking time of the target vehicle is within a preset time period, determine whether the remaining battery power is lower than a preset battery power threshold.

[0043] If the remaining battery power is lower than a preset battery power threshold, a charging reminder message will be issued.

[0044] According to a second aspect disclosed in this application, a charging reminder device for a hybrid electric vehicle is provided, comprising:

[0045] The data acquisition module is used to obtain the current parking position of the target vehicle after it has been turned off and parked.

[0046] The route prediction module is used to obtain the predicted driving route of the target vehicle for the next trip based on the current parking location and the set of habitual routes of the target vehicle; wherein, the set of habitual routes includes multiple habitual driving routes, and the predicted driving route corresponds to one of the habitual driving routes;

[0047] The energy consumption prediction module is used to obtain the predicted power consumption of the predicted driving route based on the historical power consumption data of the habitual driving route corresponding to the predicted driving route.

[0048] The preference acquisition module is used to acquire the charging preference of the target vehicle when the remaining power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power; wherein, the charging preference includes a preference for charging or a preference for refueling.

[0049] The charging reminder module is used to issue corresponding charging reminder information based on the charging preference.

[0050] According to a third aspect disclosed in this application, an electronic device is provided, including a processor and a memory communicatively connected to the processor;

[0051] The memory stores computer-executed instructions;

[0052] The processor executes computer execution instructions stored in the memory to implement the method described in any one of the first aspects.

[0053] According to a fourth aspect disclosed in this application, a computer-readable storage medium is provided, wherein computer-executable instructions are stored therein, which, when executed by a processor, are used to implement the method described in any one of the first aspects.

[0054] According to the fifth aspect disclosed in this application, a computer program product is provided, comprising a computer program that, when executed by a processor, is used to implement the method described in any one of the first aspects.

[0055] Compared with the prior art, this application has the following beneficial effects:

[0056] The hybrid vehicle charging reminder method, device, equipment, and storage medium provided in this application obtain the current parking location of the target vehicle when it is turned off and parked; based on the current parking location and the target vehicle's set of habitual routes, obtain the predicted driving route for the target vehicle's next trip; wherein the set of habitual routes includes multiple habitual driving routes, and the predicted driving route corresponds to one of the habitual driving routes; based on the historical power consumption data of the habitual driving route corresponding to the predicted driving route, obtain the predicted power consumption of the predicted driving route; when the remaining power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power, obtain the target vehicle's charging preference; wherein the charging preference includes a preference for charging or a preference for refueling; and based on the charging preference, issue corresponding charging reminder information. This achieves the technical effect of more accurately issuing charging reminders to users, making vehicle charging reminders more intelligent, meeting users' usage and travel habits, and improving user experience. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without inventive effort. Wherein:

[0058] Figure 1 A flowchart illustrating a charging reminder method for a hybrid electric vehicle provided in an embodiment of this application;

[0059] Figure 2 A flowchart illustrating another charging reminder method for a hybrid electric vehicle provided in this application embodiment;

[0060] Figure 3 A schematic diagram of a process for obtaining a habitual driving route provided in an embodiment of this application;

[0061] Figure 4 A schematic diagram of the structure of a charging reminder device for a hybrid electric vehicle provided in an embodiment of this application;

[0062] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0063] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0064] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0065] Currently, vehicle charging reminders for users are basically based on a set charging threshold. Users will only be reminded to charge when the remaining battery or fuel level falls below a fixed threshold.

[0066] Because electric and hybrid vehicles are battery-powered and have limited range, users often experience significant range anxiety. This leads to higher charging thresholds, resulting in frequent charging and unnecessarily increasing the number of charges. Excessive shallow charging and discharging prevents the battery management system (BMS) from calibrating battery capacity and calculating State of Health (SOH), negatively impacting both user experience and battery lifespan.

[0067] In addition, for hybrid vehicles, besides the battery, there is also an engine. Therefore, the remaining battery power and remaining fuel have a combined impact on the remaining driving range. If the battery power is low but the fuel is sufficient, whether or not to charge will depend on the user's charging habits.

[0068] Therefore, for existing hybrid vehicles, the existing charging reminder methods cannot accurately send charging reminders to users, resulting in a low level of intelligence in charging reminders and affecting user experience.

[0069] To address the aforementioned issues, this application proposes a charging reminder method for hybrid electric vehicles. This method predicts the vehicle's energy consumption based on a predicted driving route and then issues corresponding charging reminders based on the user's charging preferences. This approach improves the accuracy of charging reminders by leveraging energy consumption prediction and enhances the user experience by incorporating user charging preferences.

[0070] The technical solution of the charging reminder method for hybrid electric vehicles provided in this application will be described in detail below through specific embodiments. It should be noted that the following embodiments may exist alone or in combination with each other, and the same or similar content may not be described again in different embodiments.

[0071] It should be noted that the execution subject of the charging reminder method for hybrid electric vehicles provided in this application embodiment can be a vehicle system or a cloud server. When the execution subject is a cloud server, the cloud server and the target vehicle communicate and connect through vehicle networking or other means.

[0072] Compared to in-vehicle infotainment systems, cloud servers possess superior computing and data processing capabilities. Therefore, using cloud servers as the execution source results in faster execution speeds, lower hardware requirements for the vehicle, and no additional cost to the vehicle.

[0073] The cloud server may store data related to multiple vehicles, and the VIN (Vehicle Identification Number) can be used as an identification index to store the data of the corresponding vehicle.

[0074] Figure 1 A flowchart illustrating a charging reminder method for a hybrid electric vehicle provided in this application is shown below. Figure 1 In some embodiments, the charging reminder method for this hybrid vehicle includes the following steps:

[0075] S101, Obtain the current parking position of the target vehicle after it has been turned off and is parked.

[0076] The purpose of obtaining vehicle parking locations is to predict the vehicle's next travel route. Generally speaking, a user's parking location can reflect their travel habits. For example, if a user parks their vehicle in the company parking lot, their next trip is likely to be to drive home. Therefore, the user's next travel route can be predicted based on the current parking location, specifically, the user's next travel route from the company to home.

[0077] Specifically, the current parking location is the location where the vehicle was turned off, which can be obtained based on positioning systems such as GPS and BeiDou satellite navigation system.

[0078] S102, based on the current parking location and the set of habitual routes of the target vehicle, obtain the predicted driving route of the target vehicle for the next trip; wherein, the set of habitual routes includes multiple habitual driving routes, and the predicted driving route corresponds to one of the habitual driving routes.

[0079] Specifically, by obtaining the predicted route for the vehicle's next trip, and using this predicted route to determine the predicted energy consumption, the system can assess whether the vehicle's remaining battery or fuel level is sufficient to meet the user's travel needs. This allows for a more intelligent prediction of energy consumption for the next trip, enabling more accurate charging reminders to the user. The predicted route is determined based on the user's previously used routes, which are the routes the user frequently travels. Therefore, the user is highly likely to choose a familiar route for their next trip.

[0080] S103, based on the historical power consumption data of the habitual driving route corresponding to the predicted driving route, obtain the predicted power consumption of the predicted driving route.

[0081] After obtaining the predicted driving route, since the predicted driving route corresponds to a habitual driving route, which is the route that the user has frequently driven before, the energy consumption of this predicted driving route can be predicted based on the historical energy consumption data of this habitual driving route, thus obtaining the predicted power consumption of the predicted driving route.

[0082] S104. If the remaining power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power, obtain the charging preference of the target vehicle; wherein, the charging preference includes a preference for charging or a preference for refueling.

[0083] Specifically, if the vehicle's remaining battery power is less than the sum of the predicted power consumption and the preset safety margin, it indicates that the vehicle's remaining battery power is insufficient to complete the predicted driving route. However, for hybrid vehicles, the remaining battery power and remaining fuel actually have a combined impact on the remaining driving range. If the battery power is low but the fuel is sufficient, whether charging is needed depends on the user's charging habits. Obtaining the vehicle's charging preferences in this case aims to determine the method of reminding the user to charge based on those preferences, avoiding unnecessary charging reminders.

[0084] Specifically, the preset safety redundancy is to ensure that the vehicle still has a certain amount of battery power when it reaches its destination, thus avoiding a situation where the battery runs out of power.

[0085] S105 sends out corresponding charging reminder messages based on charging preferences.

[0086] If the user prefers charging, a charging reminder will be sent. If the user prefers refueling, it is necessary to further determine whether the remaining fuel is sufficient to complete the predicted driving route before issuing a refueling reminder.

[0087] In this embodiment, the following steps are taken: First, the current parking location of the target vehicle (with the engine off) is obtained. Then, based on the current parking location and the target vehicle's set of habitual routes, a predicted route for the target vehicle's next trip is obtained. The set of habitual routes includes multiple routes, and the predicted route corresponds to one of these routes. Next, based on historical power consumption data of the corresponding habitual route, the predicted power consumption of the predicted route is obtained. Finally, if the target vehicle's remaining battery power is less than the sum of the predicted power consumption and a preset safety redundancy power, the target vehicle's charging preference is obtained. This charging preference includes a preference for charging or refueling. Based on the charging preference, corresponding charging reminders are issued. This method achieves a more accurate charging reminder to the user, making vehicle charging reminders more intelligent, meeting the user's usage and travel habits, and improving the user experience.

[0088] In some embodiments, after obtaining the current parking location of the target vehicle when it is turned off and parked, the method further includes: if the current parking location is a historical charging location and the current parking time of the target vehicle is within a preset time period, determining whether the remaining battery power is lower than a preset battery power threshold; if the remaining battery power is lower than the preset battery power threshold, issuing a charging reminder message.

[0089] In this embodiment, if the current parking location is a historical charging location and the current parking time is within a preset time period, it indicates that the vehicle is currently near a charging station, and the time is a preset time period convenient for the user to charge. Specifically, the preset time period is a time period set by the user based on their own charging conditions, such as 20:00-22:00.

[0090] If the vehicle's remaining battery level is below a preset reminder threshold, the user will be immediately reminded to charge. Since this is a time when charging is readily available, the user can easily complete the charging operation based on the reminder. This makes the reminder more intelligent and greatly improves the user experience.

[0091] Specifically, the content of the charging reminder message could be something like, "To ensure your travel needs are met, please charge your phone promptly."

[0092] exist Figure 1 Based on the embodiments shown, the following is combined with Figure 2 The technical solution for the above-mentioned charging reminder method for hybrid electric vehicles will be further introduced.

[0093] Figure 2 A flowchart illustrating another charging reminder method for hybrid vehicles provided in this application embodiment is shown below. Figure 2 In some embodiments, the charging reminder method for this hybrid vehicle includes the following steps:

[0094] S201, Obtain the current parking position of the target vehicle after it has been turned off and is parked.

[0095] It should be noted that the execution process of step S201 is the same as that of step S101, and will not be repeated here.

[0096] S202: Starting from the current parking position, find the usual driving routes with the same starting point in the set of usual routes of the target vehicle to obtain the initial set of routes.

[0097] In this embodiment, the predicted route for the vehicle's next trip is determined by using familiar driving routes. Since the familiar driving routes are found based on the parking location, there may be multiple familiar driving routes that meet the same parking location conditions. Therefore, further filtering of the driving routes in the initial route set is required.

[0098] S203: Select the most frequently used route from the initial route set with the highest historical travel probability value as the predicted route for the target vehicle's next trip.

[0099] In this process, the routes in the initial route set are further filtered using historical travel probability values ​​to select the most frequently used routes with the highest historical travel probability values ​​as the predicted routes for the vehicle's next trip, which can improve the accuracy of the predicted routes as much as possible.

[0100] S204, obtain the historical power consumption of the habitual driving route corresponding to the predicted driving route.

[0101] Since the predicted driving route is a commonly used driving route, it should have a lot of historical power consumption data. Through this historical power consumption data, the energy consumption of driving the predicted driving route can be effectively reflected. Using the historical power consumption of the commonly used driving route to predict the power consumption of the predicted driving route will also result in a more accurate predicted power consumption.

[0102] S205, obtain the average historical power consumption as the predicted power consumption for the predicted driving route.

[0103] While it is known that the remaining battery range calculated by the VCU (Vehicle Control Unit) at the current parking time can be used to determine whether the predicted driving route can be completed, it is well known that the actual remaining battery range is highly correlated with many influencing factors such as the vehicle speed. Directly using the remaining range of the VCU cannot perfectly predict the actual distance that the remaining battery power can travel on a specific route on the next trip. For example, if the predicted driving route is a low-speed congested route, the actual range that the battery can travel will be greater than the remaining range predicted by the VCU.

[0104] Therefore, using the average historical power consumption of the commonly used driving route corresponding to the predicted driving route as the predicted power consumption of the predicted driving route avoids the great impact of factors such as changes in vehicle speed on the remaining range of pure electric vehicles, and can improve the accuracy of predicting the power consumption of the predicted driving route.

[0105] S206: When the remaining battery power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power, obtain charging preference data and refueling preference data within a preset statistical period; wherein, the charging preference data and refueling preference data are the number of charging and refueling times, pure electric driving mileage and hybrid driving mileage, or historical power consumption and historical fuel consumption.

[0106] Among them, the vehicle's charging preference is reflected by relevant charging preference data. By obtaining the corresponding charging preference data and refueling preference data from the charging preference data, the charging preference can be determined in the future.

[0107] Specifically, the preset statistical period can be a time period or a mileage period, such as the number of times charging and refueling within a week, or the number of times charging and refueling within 100 kilometers.

[0108] Specifically, charging frequency, pure electric driving mileage, and historical energy consumption are charging preference data that reflect charging preferences, while refueling frequency, hybrid driving mileage, and historical fuel consumption are refueling preference data that reflect refueling preferences.

[0109] S207. The charging method corresponding to the preference data with the highest proportion or the proportion reaching the preset proportion value in the statistical charging preference data and refueling preference data is used as the charging preference of the target vehicle.

[0110] Among them, statistical analysis of the corresponding charging preference data and refueling preference data can reflect the charging preference of the target vehicle.

[0111] Taking the number of charging and refueling trips as charging preference data, the more times a certain charging method is used, the more the user prefers that method. By statistically analyzing the number of times different charging methods are used in historical charging history, the vehicle's charging preference can be obtained. Specifically, there are two ways to determine a vehicle's charging preference: one is to select the charging method with the most trips, i.e., the charging method with the most trips in both charging and refueling trips is considered the vehicle's charging preference; the other is to select the charging method that reaches a preset percentage. For example, if the preset percentage is 60%, then the charging method must account for 60% of the historical charging trips to be considered the vehicle's charging preference. If no charging method reaches the preset percentage, it indicates that the user does not have a clear charging preference, and in this case, the charging method with the most trips can be selected as the charging preference.

[0112] Similar to charging and refueling frequency, pure electric driving range and hybrid driving range, as well as historical energy consumption and historical fuel consumption, can also be used to determine charging preferences. For example, if the pure electric driving range per 100 kilometers is greater than the hybrid driving range, it indicates that the user prefers to drive in pure electric mode, which in turn indicates that the user prefers to charge. Similarly, if the historical energy consumption per 100 kilometers is greater than the historical fuel consumption, it also indicates that the user prefers to drive in pure electric mode, which in turn indicates that the user prefers to charge.

[0113] In addition, users can also determine their charging preferences by directly setting their preferred charging or refueling method on the target vehicle.

[0114] S208 issues a charging reminder message when the charging preference is set to preferred charging.

[0115] This feature allows for sending charging reminders when users prefer to charge their devices. For example, it could say, "Please charge your device promptly to ensure your travel needs are met."

[0116] Specifically, charging reminders can be sent to users via mobile phone messages or in-vehicle displays.

[0117] S209, when the charging preference is to refuel, obtains the predicted fuel consumption of the target vehicle based on the predicted driving route and remaining battery power.

[0118] When a user prefers to refuel, it is also necessary to predict the vehicle's fuel consumption to determine whether the remaining fuel is sufficient for travel needs. Therefore, the first priority is to obtain the predicted fuel consumption after the vehicle completes the predicted driving route.

[0119] Preferably, the predicted fuel consumption of the target vehicle is obtained based on the predicted driving route and the remaining battery power, including: inputting the predicted driving route and the remaining battery power into the fuel consumption prediction model to obtain the predicted fuel consumption; wherein the fuel consumption prediction model is obtained by simulation based on the vehicle data of the target vehicle, or by machine learning training based on the historical travel data of the target vehicle.

[0120] In this embodiment, the fuel consumption prediction model can be obtained in two ways. One way is to simulate the target vehicle's data. In this case, the predicted driving route and remaining battery power are input, and the fuel consumption prediction model can simulate the target vehicle's driving process and obtain the predicted fuel consumption based on the simulation results. The other way is to use the target vehicle's historical travel data as the training set to train the machine learning model. After training, a fuel consumption prediction model is obtained with the predicted driving route and remaining battery power as input and the predicted fuel consumption as output.

[0121] Furthermore, existing hybrid electric vehicles (HEVs) may also incorporate predictive energy management (REM) technology. REM is an advanced energy control technology used in HEVs. It collects information such as traffic conditions, vehicle speed, gradients, traffic lights, and distances to other vehicles along the route. Using REM algorithms, it optimizes the battery's energy consumption curve to determine the optimal range for reaching the destination, thus allowing the engine to operate more within its fuel-efficient range, achieving fuel savings and emissions reduction. Different driving routes, vehicle speeds, and the initial battery state all significantly impact the fuel consumption of HEVs equipped with REM. Essentially, REM utilizes predictions of when the engine will experience inefficiencies throughout the journey. It pre-charges the battery during the engine's efficient range and uses the pre-set battery power for pure electric drive during the inefficiencies, thereby improving overall fuel efficiency for the entire trip.

[0122] Hybrid vehicles equipped with predictive energy management technology significantly reduce fuel consumption and increase real-world driving range. This makes it difficult for users to judge when to refuel based solely on the instrument panel's fuel range, often increasing refueling frequency and causing inconvenience. When predictive energy management technology intervenes in the powertrain control during a trip, the amount of electricity and fuel consumed for the same distance will be less compared to when there is no predictive energy intervention. The degree of this reduction is related to the route, traffic congestion, and the battery's initial charge level, and cannot be derived using simple formulas or logic. This makes it difficult for users to accurately estimate whether the remaining battery and fuel are sufficient for their next trip.

[0123] Therefore, when hybrid vehicles are equipped with predictive energy management technology, traditional solutions generally use average values ​​or lookup tables to calculate the fuel consumption and remaining driving range of hybrid vehicles with predictive energy management technology. In reality, the fuel consumption of vehicles equipped with predictive energy management technology is highly dependent on the travel route and the battery SOC (State of Charge) at the time of departure, and is not a constant value. Traditional solutions are very inaccurate in predicting fuel consumption and range, often overestimating or underestimating.

[0124] In this embodiment, when the hybrid vehicle is equipped with predictive energy management technology, for the fuel consumption prediction model, if the fuel consumption prediction model is obtained through simulation based on the target vehicle's vehicle data, then the fuel consumption prediction model should predict the vehicle's fuel consumption when the predictive energy management function is enabled, and the model's input information also needs to include route information for the predicted driving route. If the fuel consumption prediction model is obtained through machine learning training based on the target vehicle's historical travel data, then during the model training phase, it is also necessary to acquire historical travel data under the predictive energy management technology to train the fuel consumption prediction simulation model, and the model's input information also needs to include route information for the predicted driving route. Specifically, the route information includes traffic information, vehicle speed information, slope information, traffic light information, and distance to vehicles ahead along the predicted driving route.

[0125] In this way, the fuel consumption prediction model can also be adapted to predict the fuel consumption of vehicles under the predictive energy management technology, thereby improving the accuracy and adaptability of the fuel consumption prediction simulation model.

[0126] Specifically, the fuel consumption prediction model is deployed on a cloud server, and its accuracy can be continuously improved through iteration, thereby enhancing the user experience.

[0127] S210 issues a refueling reminder when the remaining fuel in the target vehicle is less than the predicted fuel consumption.

[0128] If the remaining fuel is insufficient to complete the predicted route, a refueling reminder message will be sent, such as, "Please refuel in time to ensure your travel needs are met."

[0129] Specifically, refueling reminders can be sent to users via mobile phone messages or in-vehicle displays.

[0130] In this embodiment, the vehicle's next travel route is predicted based on the user's habitual driving route. This analysis of user travel patterns allows for the planning of charging times, reducing the frequency of user charging compared to traditional fixed-limit charging reminders. By maximizing the user's charging intervals, the battery's capacity change during each charge is maximized, which is beneficial for BMS control and SOH calculation. This avoids damage to the battery from excessive shallow charging and discharging, extending battery life.

[0131] Furthermore, the system considers the remaining fuel and battery levels of hybrid vehicles, as well as the frequency of charging to determine charging preferences. It also plans charging times based on users' fuel and electricity usage habits, maximizing both charging and refueling intervals. By combining energy consumption prediction with charging preferences, the system can more accurately send charging reminders to users, making charging reminders more intelligent, meeting users' usage and travel habits, and improving the user experience.

[0132] exist Figure 1 and Figure 2 The charging reminder method for hybrid vehicles shown requires obtaining a set of frequently used routes. The following section will explain this in conjunction with... Figure 3 The technical solution for the above-mentioned charging reminder method for hybrid electric vehicles will be further described regarding the acquisition of the set of commonly used routes.

[0133] Figure 3 This application provides a schematic diagram of a process for obtaining a habitual driving route, as illustrated in the embodiments of this application. Figure 3 In some embodiments, the process of obtaining the preferred route includes the following steps:

[0134] S301, acquire historical driving data of the target vehicle within a preset collection period.

[0135] Among them, obtaining the historical driving data of the vehicle within the preset collection period means collecting the historical driving data of the vehicle at fixed intervals. For example, if the preset collection period is one day, the historical driving data will be collected once every day; if the preset collection period is two days, the historical driving data will be collected once every two days.

[0136] Specifically, historical driving data generally includes information related to the starting point, destination, route, time, and energy consumption.

[0137] S302 uses the historical parking locations where the engine was turned off and parked as nodes to obtain travel routes from historical driving data.

[0138] Within a single data collection period, there may be multiple travel routes. Therefore, it is necessary to extract travel routes from historical driving data and aggregate all extracted travel routes into a travel route set.

[0139] Specifically, the extraction of travel routes can be based on the parking location where the vehicle is parked and turned off as the determination node. The travel route is defined as the distance between two historical parking nodes from when the vehicle starts from a parked state and when it stops and turns off again.

[0140] S303 obtains the starting point, ending point, and driving information for each travel route; the driving information includes traffic information and vehicle information.

[0141] The collected travel routes need to be compared with historical travel routes obtained in previous collection periods to match the same historical travel routes and further determine which historical travel routes can be used as frequently used travel routes. Therefore, it is necessary to obtain relevant information about historical travel routes for subsequent matching of the same historical travel routes.

[0142] Specifically, traffic information includes road information, distance information, traffic light information, slope information, and other information that reflects traffic conditions.

[0143] Specifically, vehicle information includes information that reflects the vehicle's condition, such as vehicle speed, time, engine speed, fuel consumption, mileage, battery charge, and oil pressure.

[0144] S304: Search for historical routes with the same start and end points in the historical route set to obtain a candidate route set.

[0145] Routes with the same start and end points are more likely to belong to the same route. Therefore, using the start and end points as initial filtering criteria, historical routes that meet these criteria are initially screened. This allows for the rapid identification of historical routes that do not meet the criteria, improving matching efficiency.

[0146] S305, compare the similarity between the driving information of historical driving routes in the candidate route set and the driving information of the travel route, and increment the historical travel count value of the historical driving routes that meet the preset similarity conditions by one.

[0147] Further filtering is performed based on the travel information to identify the corresponding historical travel routes in the historical travel route set, and to determine whether there are any historical travel routes in the historical travel route set that are the same as the travel route.

[0148] Specifically, similarity filtering converts historical driving route information into feature vectors, and then uses these feature vectors to obtain the similarity between the two routes. Common similarity judgment methods include Euclidean distance and cosine similarity. The similarity value range is generally [0,1]. By setting a similarity threshold, such as 0.9, if the similarity exceeds 0.9, the similarity can be considered to meet the preset conditions.

[0149] In this case, incrementing the historical trip count value of the selected historical routes by one indicates that the trip count has increased by one.

[0150] S306 divides historical driving routes with the same starting point into the same subset of historical driving routes.

[0151] In the set of historical driving routes, there may be multiple historical driving routes starting from the same starting point. Therefore, historical driving routes with the same starting point are divided into the same subset of historical driving routes for use in determining subsequent commonly used driving routes.

[0152] S307. For each historical route in each historical route subset, calculate the ratio between the historical number of trips for that historical route and the total number of historical trips in the historical route subset to obtain the historical trip probability value for that historical route.

[0153] Specifically, the historical travel probability value is calculated for each historical travel route within a subset of historical travel routes. This is achieved by taking the ratio of the number of historical trips for a given historical travel route to the total number of historical trips for all historical travel routes originating from the same point in time. A higher historical travel probability value indicates that the corresponding historical travel route is more frequently used.

[0154] Specifically, for example, if there are three historical routes in the historical driving subset, the total number of historical trips for the three historical routes is 20, and one of the historical routes has 12 trips, its historical trip probability value is 0.6. If the preset probability threshold is 0.5, then this historical route meets the condition and will be added to the set of habitual routes as a frequently used route.

[0155] S308 adds historical travel routes with a probability value greater than a preset probability threshold to the set of frequently used routes.

[0156] Among them, when the historical travel probability value of a historical driving route is greater than a preset probability threshold, it indicates that it meets the driving frequency condition of a commonly used driving route, and then it is added to the commonly used route set as a commonly used driving route.

[0157] In addition, it should be noted that historical travel routes that match the usual travel routes will not be removed from the historical travel route set. In subsequent data collection periods, if the historical travel route can still match the subsequent travel routes, its historical travel frequency value can continue to increase.

[0158] S309: If there are no historical routes with the same start and end points in the historical route set, or if there are no historical routes with similarity of driving information that meet the preset conditions in the candidate route set, the travel route shall be added to the historical route set.

[0159] If no corresponding historical route is found in the historical route set, it indicates that the route is appearing for the first time. Once a route is determined to be appearing for the first time, it is added to the historical route set for use in determining subsequent frequently used routes.

[0160] In this embodiment, the vehicle's habitual route set is obtained using a probabilistic approach to predict the vehicle's next travel route. Compared to training a big data model to predict travel routes, this embodiment saves more computing resources, runs faster, and is less expensive.

[0161] Figure 4 This is a schematic diagram of a charging reminder device for a hybrid electric vehicle provided in an embodiment of this application. (See attached diagram.) Figure 4 The charging reminder device for the hybrid electric vehicle includes various functional modules for implementing the aforementioned charging reminder method for the hybrid electric vehicle. Any functional module can be implemented by software and / or hardware.

[0162] In some embodiments, the charging reminder device 400 for a hybrid electric vehicle includes an information acquisition module 401, a route prediction module 402, an energy consumption prediction module 403, a preference acquisition module 404, and a charging reminder module 405. Wherein:

[0163] The data acquisition module 401 is used to acquire the current parking position of the target vehicle when it is turned off and parked.

[0164] The route prediction module 402 is used to obtain the predicted driving route of the target vehicle for the next trip based on the current parking location and the set of habitual routes of the target vehicle; wherein, the set of habitual routes includes multiple habitual driving routes, and the predicted driving route corresponds to one of the habitual driving routes.

[0165] The energy consumption prediction module 403 is used to obtain the predicted power consumption of the predicted driving route based on the historical power consumption data of the habitual driving route corresponding to the predicted driving route.

[0166] The preference acquisition module 404 is used to acquire the charging preference of the target vehicle when the remaining power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power; wherein, the charging preference includes a preference for charging or a preference for refueling.

[0167] The charging reminder module 405 is used to send corresponding charging reminder information according to charging preferences.

[0168] In some embodiments, the route prediction module 402 is specifically used for:

[0169] Starting from the current parking location, find the habitual driving routes with the same starting point in the target vehicle's habitual route set to obtain a preliminary route set;

[0170] The most frequently used route in the initial route set with the highest historical travel probability value and number of trips is selected as the predicted route for the target vehicle's next trip.

[0171] In some embodiments, the route prediction module 402 is specifically used for:

[0172] Acquire historical driving data of the target vehicle within a preset collection period;

[0173] Using historical parking locations where the engine was turned off as nodes, the travel route is obtained from the historical driving data;

[0174] For each travel route, the similarity of the travel route is compared with the historical travel routes in the target vehicle's historical travel route set, and the number of historical trips for historical travel routes that meet the preset similarity conditions is incremented by one;

[0175] Historical routes with the same starting point are grouped into the same subset of historical routes.

[0176] For each historical route in each subset of historical routes, calculate the ratio between the number of historical trips for that historical route and the total number of historical trips in the subset of historical routes to obtain the historical trip probability value for that historical route.

[0177] Historical travel routes with a probability value greater than a preset probability threshold are added to the set of frequently used routes.

[0178] In some embodiments, the route prediction module 402 is specifically used for:

[0179] Obtain the starting point, destination, and travel information of the travel route; the travel information includes traffic information and vehicle information.

[0180] Search the historical driving route set for historical driving routes with the same start and end points to obtain a candidate route set;

[0181] The similarity of the driving information of historical driving routes in the candidate route set with the driving information of the travel route is compared, and the number of historical trips of historical driving routes that meet the preset similarity conditions is incremented by one.

[0182] In some embodiments, the route prediction module 402 is specifically used for:

[0183] If there are no historical routes with the same start and end points in the historical route set, or if there are no historical routes in the candidate route set that meet the preset conditions for similarity of driving information, it is determined that there is no historical route in the historical route set that corresponds to the travel route, and the travel route is added to the historical route set.

[0184] In some embodiments, the energy consumption prediction module 403 is specifically used for:

[0185] Obtain the historical power consumption of the habitual driving route corresponding to the predicted driving route;

[0186] Obtain the average historical power consumption as the predicted power consumption for the predicted driving route.

[0187] In some embodiments, the preference acquisition module 404 is specifically used for:

[0188] Obtain charging preference data and refueling preference data within a preset statistical period; wherein, the charging preference data and refueling preference data are the number of charging times and refueling times, pure electric driving mileage and hybrid driving mileage, or historical electricity consumption and historical fuel consumption;

[0189] The charging method corresponding to the preference data with the highest proportion or the proportion reaching the preset value in the statistical charging preference data and refueling preference data is taken as the charging preference of the target vehicle.

[0190] In some embodiments, the charging reminder module 405 is specifically used for:

[0191] If the charging preference is set to preferred charging, a charging reminder message will be sent.

[0192] When the charging preference is to refuel, the predicted fuel consumption of the target vehicle is obtained based on the predicted driving route and remaining battery power.

[0193] A refueling reminder will be sent if the remaining fuel in the target vehicle is less than the predicted fuel consumption.

[0194] In some embodiments, the energy consumption prediction module 403 is specifically used for:

[0195] Input the predicted driving route and remaining battery power into the fuel consumption prediction model to obtain the predicted fuel consumption;

[0196] The fuel consumption prediction model is obtained through simulation based on the target vehicle's vehicle data, or through machine learning training based on the target vehicle's historical travel data.

[0197] In some embodiments, the charging reminder module 405 is specifically used for:

[0198] If the current parking location is a historical charging location and the current parking time of the target vehicle is within a preset time period, determine whether the remaining battery power is lower than a preset battery power threshold.

[0199] A charging reminder will be sent when the remaining battery level is lower than a preset battery threshold.

[0200] The charging reminder device 400 for hybrid electric vehicles provided in this application embodiment is used to execute the technical solution provided in the aforementioned charging reminder method embodiment for hybrid electric vehicles. Its implementation principle and technical effects are similar to those in the aforementioned method embodiment, and will not be repeated here.

[0201] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing elements, entirely in hardware, or partially in software via processing elements and partially in hardware. For example, the route prediction module can be a separate processing element, or it can be integrated into a chip within the device. Alternatively, it can be stored as program code in the device's memory, invoked and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the processor element or through software instructions.

[0202] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. (See attached diagram.) Figure 5 The electronic device 500 includes: a processor 501, and a memory 502 communicatively connected to the processor 501;

[0203] Memory 502 stores instructions executed by the computer;

[0204] The processor 501 executes computer execution instructions stored in the memory 502 to implement the aforementioned technical solution of the charging reminder method for hybrid electric vehicles.

[0205] In the aforementioned electronic device 500, the memory 502 and the processor 501 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as bus connections. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be classified as address buses, data buses, control buses, etc., but this does not mean that there is only one bus or one type of bus. The memory 502 stores computer execution instructions for implementing the aforementioned charging reminder method for hybrid electric vehicles, including at least one software functional module that can be stored in the memory in the form of software or firmware. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory.

[0206] The memory 502 includes at least one type of readable storage medium, not limited to Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory 502 stores programs, and the processor 501 executes the programs after receiving execution instructions. Furthermore, the software programs and modules within the memory 502 may also include an operating system, which may include various software components and / or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components.

[0207] Processor 501 can be an integrated circuit chip with signal processing capabilities. The aforementioned processor 501 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or processor 501 can be any conventional processor.

[0208] The electronic device 500 is used to execute the technical solution provided in the aforementioned embodiment of the charging reminder method for hybrid electric vehicles. Its implementation principle and technical effects are similar to those in the aforementioned method embodiment, and will not be repeated here.

[0209] This application also provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the aforementioned charging reminder method for hybrid electric vehicles.

[0210] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0211] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. Both the processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the control unit of a charging reminder device in a hybrid electric vehicle.

[0212] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the aforementioned technical solution for a charging reminder method for hybrid electric vehicles.

[0213] In the above embodiments, those skilled in the art will understand that the above method embodiments can be implemented entirely or partially by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless network, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0214] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0215] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method of charging reminder for a hybrid vehicle, characterized by, include: Obtain the current parking position of the target vehicle after it has been turned off and is parked. Based on the current parking location and the set of habitual routes of the target vehicle, the predicted driving route for the next trip of the target vehicle is obtained; wherein, the set of habitual routes includes multiple habitual driving routes, and the predicted driving route corresponds to one of the habitual driving routes. Based on the historical power consumption data of the habitual driving route corresponding to the predicted driving route, the predicted power consumption of the predicted driving route is obtained. If the remaining battery power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power, the charging preference of the target vehicle is obtained; wherein, the charging preference includes a preference for charging or a preference for refueling. If the charging preference is set to preferred charging, a charging reminder message will be issued. When the charging preference is to refuel, the predicted driving route and the remaining battery power are input into the fuel consumption prediction model to obtain the predicted fuel consumption of the target vehicle; wherein, the fuel consumption prediction model is used to adapt the fuel consumption prediction of the vehicle under the predictive energy management technology. If the remaining fuel level of the target vehicle is less than the predicted fuel consumption, a refueling reminder will be issued.

2. The method of claim 1, wherein, Based on the current parking location and the target vehicle's habitual route set, obtain the predicted driving route for the target vehicle's next trip, including: Starting from the current parking location, search for the usual driving routes with the same starting point in the set of usual routes of the target vehicle to obtain a preliminary set of routes; The most frequently used route with the highest historical travel probability value in the initial route set is selected as the predicted travel route for the target vehicle's next trip.

3. The method of claim 2, wherein, The method for obtaining the set of commonly used routes includes: Acquire historical driving data of the target vehicle within a preset collection period; Using the historical parking locations where the engine was turned off and parked in the historical driving data as nodes, the travel route is obtained from the historical driving data; For each travel route, the similarity of the travel route is compared with the historical travel routes in the historical travel route set of the target vehicle, and the number of historical trips of the historical travel routes that meet the preset similarity conditions is incremented by one; Historical routes with the same starting point in the set of historical routes are divided into the same subset of historical routes; For each historical route in each subset of historical routes, the ratio between the number of historical trips for that historical route and the total number of historical trips in the subset of historical routes is calculated to obtain the historical trip probability value for that historical route. Historical travel routes with a probability value greater than a preset probability threshold are added to the set of commonly used routes.

4. The method of claim 3, wherein, The similarity between the stated travel route and the historical travel routes in the set of historical travel routes of the target vehicle is compared, and the number of historical trips for historical travel routes that meet the preset similarity conditions is incremented by one, including: Obtain the starting point, ending point, and travel information of the travel route; wherein the travel information includes traffic information and vehicle information; Search the set of historical driving routes for those with the same start and end points to obtain a set of candidate routes. The similarity of the driving information of historical routes in the candidate route set with the driving information of the travel route is compared, and the number of historical trips of historical routes that meet the preset similarity conditions is incremented by one.

5. The method of claim 4, wherein, The method further includes: If there are no historical routes with the same start and end points in the historical route set, or if there are no historical routes in the candidate route set that meet the preset conditions for similarity of driving information, the travel route will be added to the historical route set.

6. The method of claim 1, wherein, Based on historical power consumption data of the habitual driving route corresponding to the predicted driving route, the predicted power consumption of the predicted driving route is obtained, including: Obtain the historical power consumption of the habitual driving route corresponding to the predicted driving route; The average value of the historical power consumption is obtained as the predicted power consumption for the predicted driving route.

7. The method of claim 1, wherein, Obtaining the charging preferences of the target vehicle includes: Obtain charging preference data and refueling preference data within a preset statistical period; wherein, the charging preference data and the refueling preference data are the number of charging times and the number of refueling times, pure electric driving mileage and hybrid driving mileage, or historical electricity consumption and historical fuel consumption; The charging method corresponding to the preference data with the highest proportion or the proportion reaching a preset value in the charging preference data and the refueling preference data is taken as the charging preference of the target vehicle.

8. The method according to claim 1, characterized in that, The fuel consumption prediction model is obtained through simulation based on the vehicle data of the target vehicle, or through machine learning training based on the historical travel data of the target vehicle.

9. The method of claim 1, wherein, After obtaining the current parking location of the target vehicle with the engine off and parked, the following is also included: If the current parking location is a historical charging location and the current parking time of the target vehicle is within a preset time period, determine whether the remaining battery power is lower than a preset battery power threshold. If the remaining battery power is lower than a preset battery power threshold, a charging reminder message will be issued.

10. A power charging reminding device for a hybrid vehicle, characterized by comprising: include: The data acquisition module is used to obtain the current parking position of the target vehicle after it has been turned off and parked. The route prediction module is used to obtain the predicted driving route of the target vehicle for the next trip based on the current parking location and the set of habitual routes of the target vehicle; wherein, the set of habitual routes includes multiple habitual driving routes, and the predicted driving route corresponds to one of the habitual driving routes; The energy consumption prediction module is used to obtain the predicted power consumption of the predicted driving route based on the historical power consumption data of the habitual driving route corresponding to the predicted driving route. The preference acquisition module is used to acquire the charging preference of the target vehicle when the remaining power of the target vehicle is less than the sum of the predicted power consumption and the preset safety redundancy power; wherein, the charging preference includes a preference for charging or a preference for refueling. The charging reminding module is configured to, in a case where the charging preference is a preference for charging, send a charging reminding information; in a case where the charging preference is a preference for refueling, input the predicted driving route and the remaining electric quantity into an oil consumption prediction model to obtain a predicted oil consumption of the target vehicle; wherein the oil consumption prediction model is configured to adapt to oil consumption prediction of the vehicle under a prediction energy management technology; and in a case where the remaining oil quantity of the target vehicle is less than the predicted oil consumption, send a refueling reminding information.

11. An electronic device, comprising: The device comprises a processor and a memory connected in communication with the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by the processor to implement the method according to any one of claims 1 to 9.

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