Endurance prediction method, vehicle and computer readable storage medium

Abstract

The application provides a range prediction method, a vehicle and a computer readable storage medium, and relates to the technical field of vehicle range, and the method comprises the following steps: acquiring state parameters of the vehicle, road condition parameters of a future driving path of the vehicle and range energy; wherein the state parameters are parameters affecting tire rolling resistance of the vehicle; and the remaining range is predicted based on the state parameters, the road condition parameters and the range energy. The technical scheme of the application can improve the accuracy of range prediction.

Description

Technical Field

[0001] This application relates to the field of vehicle range technology, specifically to a range prediction method, a vehicle, and a computer-readable storage medium. Background Technology

[0002] Currently, the prediction of vehicle range (including fuel range and electric range) generally adopts a relatively simple method, which is mainly based on the product of the current remaining fuel and / or electricity and the historical average energy consumption (such as the average energy consumption of the last 50 / 100 kilometers). The driver is passively reminded to find a gas station / charging station only when the remaining range is lower than a certain threshold.

[0003] However, the driving range predicted using the aforementioned method differs significantly from the actual driving range, indicating insufficient prediction accuracy. Summary of the Invention

[0004] This application provides a range prediction method, a vehicle, and a computer-readable storage medium, which can improve the accuracy of range prediction.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions: Firstly, a range prediction method is provided, which includes: acquiring vehicle state parameters, road condition parameters of the vehicle's future driving path, and remaining energy; wherein, the state parameters are parameters that affect the vehicle's tire rolling resistance; and predicting the remaining driving range based on the state parameters, road condition parameters, and remaining energy.

[0006] In this application, real-time physical conditions (such as vehicle load and tire condition) and future road resistance (such as slope and curve) that affect vehicle energy consumption are used as parameters affecting the predicted driving range, thereby enabling a more accurate simulation of the vehicle's actual driving energy consumption and significantly improving the accuracy of driving range prediction.

[0007] In conjunction with the first aspect, in one possible design approach, the remaining driving range is predicted based on state parameters, road condition parameters, and remaining energy, including: predicting the average energy consumption on a future driving path based on state parameters and road condition parameters; wherein the future driving path is the path from the current location to the destination; and determining the vehicle's remaining driving range based on remaining energy and average energy consumption.

[0008] In this application, the vehicle can predict the average energy consumption on a future driving path based on state parameters and road condition parameters. Then, based on the remaining driving energy and the average energy consumption, the vehicle's remaining driving range is determined. This comprehensively considers the state parameters and road condition parameters of the overall path, avoiding the generation of instantaneous average energy consumption based on instantaneous state parameters and road condition parameters, thus improving the accuracy of average energy consumption prediction and consequently improving the accuracy of remaining driving range prediction. If the remaining driving energy is sufficient for the vehicle to reach its destination, no message is displayed to inform the user that the vehicle cannot reach its destination using the current driving energy. If the remaining driving energy is insufficient for the vehicle to reach its destination, a message is displayed to inform the user that the vehicle cannot reach its destination using the current driving energy, thereby improving the reliability of energy replenishment reminders.

[0009] In conjunction with the first aspect, in one possible design approach, based on state parameters and road condition parameters, the average energy consumption on the future driving path is predicted, including: dividing the future driving path into a preset number of road segments; determining the road condition parameters corresponding to each of the preset number of road segments; predicting the energy consumption of each road segment based on the state parameters and the road condition parameters corresponding to each road segment; determining the total energy consumption of the future driving path based on the energy consumption of each road segment; and predicting the average energy consumption on the future driving path based on the total energy consumption and the path distance corresponding to the future driving path.

[0010] In this application, the continuous and complex future driving path is divided into preset road segments, and the road condition parameters of each road segment are determined. Combined with the current state parameters of the vehicle and the road condition parameters of the road segment, the energy consumption of each road segment is predicted. This largely restores the real driving scenario and can accurately predict the energy consumption of each road segment, thereby accurately predicting the average energy consumption on the future driving path.

[0011] In conjunction with the first aspect, in one possible design approach, the energy consumption of each road segment is predicted based on the state parameters and the road condition parameters corresponding to each road segment. This includes: inputting the state parameters and the road condition parameters corresponding to each road segment into the power consumption prediction model, and using the power consumption prediction model to predict the energy consumption of each road segment.

[0012] In this application, by combining state parameters reflecting the real-time operating status of the vehicle with road condition parameters reflecting the characteristics of the external environment, the energy consumption prediction model is made adaptive to actual operating conditions, which significantly improves the accuracy and reliability of energy consumption prediction and provides accurate data support for range replenishment reminders.

[0013] In conjunction with the first aspect, in one possible design approach, the state parameters include the tire pressure and temperature of the vehicle's tires. A power consumption prediction model is used to predict the average energy consumption along future driving paths. This includes: the power consumption prediction model determining a load correction coefficient based on tire pressure; wherein the load correction coefficient is used to adjust the vehicle's preset load parameters in the power consumption prediction model, and the load correction coefficient is positively correlated with average energy consumption; the power consumption prediction model determining a tire condition correction coefficient based on tire pressure and temperature; wherein the tire condition correction coefficient is used to adjust the tire's rolling resistance coefficient in the power consumption prediction model, and the tire condition correction coefficient is positively correlated with average energy consumption; the power consumption prediction model determining a road condition correction coefficient based on road condition parameters; wherein the road condition correction coefficient is used to adjust the preset road condition coefficient in the power consumption prediction model, and the road condition correction coefficient is positively correlated with average energy consumption; and the power consumption prediction model predicts the energy consumption of each road segment based on the load correction coefficient, tire condition correction coefficient, and road condition correction coefficient.

[0014] In this application, a three-level cascaded correction mechanism consisting of load correction coefficient, tire condition correction coefficient, and road condition correction coefficient is constructed. Based on vehicle dynamics, the basic energy consumption prediction model is dynamically corrected in multiple dimensions. While ensuring computational efficiency, the prediction error caused by fixed parameter assumptions is effectively eliminated, and the accuracy of energy consumption prediction under complex working conditions is significantly improved.

[0015] In conjunction with the first aspect, in one possible design approach, the method further includes: outputting a prompt message when the remaining driving range does not meet the remaining range condition; wherein the target mileage is the distance from the vehicle's current location to the destination, and the prompt message is used to inform the user that the vehicle cannot reach the destination using the current driving range.

[0016] In this application, based on highly accurate predicted driving range, it is possible to accurately predict situations where the remaining driving range is less than the sum of the target range and the preset range, so as to promptly remind the user and improve the reliability of the driving range reminder.

[0017] In conjunction with the first aspect, in one possible design approach, the method further includes: determining a target refueling point set, the target refueling point set including multiple target refueling points, the multiple target refueling points being refueling points that the vehicle can reach using its remaining range energy; determining refueling points from the target refueling point set that conform to preset user preference settings; displaying recommended information of refueling points that conform to preset user preference settings; wherein, refueling points that conform to preset user preference settings include any one or more of first refueling point information, second refueling point information, and third refueling point information; the first refueling point information includes a first refueling point, the second refueling point information includes a second refueling point, and the third refueling point information includes a third refueling point; the first refueling point is the refueling point corresponding to the shortest time in the target refueling point set; the second refueling point is the second refueling point closest to the vehicle's current location; and the third refueling point is the refueling point with the lowest energy price in the target refueling point set.

[0018] In this application, recommended information about energy replenishment points that match preset user preferences can be displayed, thereby improving the user experience.

[0019] In conjunction with the first aspect, in one possible design approach, a first marker ring and / or a set of controls are displayed on the navigation map; wherein, the first marker ring is a ring with the vehicle's location as the center and the remaining driving range as the radius; each control in the control set is a control located on the navigation map at the location of information about a charging point that conforms to preset user preference settings, and the control is used to respond to the user's preset operation and use the charging point corresponding to the control as a navigation waypoint or a new destination.

[0020] In this application, the first marker ring, namely the "dynamic range ring," is a visual design that intuitively displays all geographical areas the vehicle can currently reach. It transforms abstract range figures into a clear image of the geographical area, allowing the driver to immediately see the extent of their current energy range, greatly improving the user-friendliness of human-machine interaction and the ability to perceive driving situations. Users can easily set it as a navigation waypoint or new destination with a single touch, enhancing the user experience.

[0021] Secondly, embodiments of this application provide a vehicle, including: a processor; and a memory for storing processor-executable instructions, wherein the processor is used to execute the range prediction method of the first aspect described above.

[0022] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program for performing the battery life prediction method described in the first aspect.

[0023] Fourthly, embodiments of this application provide a computer program product, which includes a computer program. When the computer program is executed by the processor of a computer device, it enables the computer device to perform the battery life prediction method described in the first aspect.

[0024] The technical effects of any of the design methods in the second to fourth aspects can be found in the technical effects of different design methods in the first aspect, and will not be repeated here. Attached Figure Description

[0025] Figure 1 The diagram shown is an application scenario illustration of the range prediction method provided in an exemplary embodiment of this application.

[0026] Figure 2 The diagram shown is a flowchart illustrating an exemplary embodiment of the battery life prediction method provided in this application.

[0027] Figure 3 The diagram shown is a schematic representation of the first interface provided in an exemplary embodiment of this application.

[0028] Figure 4 The diagram shown is a structural schematic of a vehicle provided in an exemplary embodiment of this application.

[0029] Figure 5 The diagram shown is a schematic representation of the battery life prediction device provided in an exemplary embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] Application Overview As mentioned in the background section, the driving range predicted using the aforementioned method differs significantly from the actual drivable range, indicating insufficient prediction accuracy and consequently, low reliability of the driving range reminder.

[0032] Based on existing methods for predicting driving range, the applicant recognizes that historical average energy consumption only represents energy consumption over a past period and cannot reflect future energy consumption on future routes. For example, vehicle load and tire condition can change in real time; higher load and resistance increase energy consumption, while lower load and resistance decrease it. Furthermore, steeper or more winding roads increase energy consumption, while gentler slopes, fewer curves, and smoother roads result in lower energy consumption. Therefore, it is possible to predict vehicle driving range based on road conditions and parameters affecting tire rolling resistance, thereby reducing the gap between the actual driving range and the predicted range and improving prediction accuracy.

[0033] Specifically, in response to the above-mentioned technical problems, this application provides a method comprising: acquiring vehicle state parameters, road condition parameters of the vehicle's future driving path, and remaining driving energy; wherein, the state parameters are parameters that affect the rolling resistance of the vehicle's tires; and predicting the remaining driving range based on the state parameters, road condition parameters, and remaining driving energy.

[0034] In this embodiment of the application, real-time physical conditions (such as vehicle load and tire condition) and future road resistance (such as slope and curve) that affect vehicle energy consumption are used as parameters affecting the predicted driving range. This allows for a more accurate simulation of the vehicle's actual driving energy consumption, significantly improving the accuracy of the driving range prediction. The predicted driving range is based on this higher accuracy.

[0035] Exemplary scenario The range prediction method provided in this application can be applied to various vehicles. Vehicles include various types of vehicles, such as sedans, SUVs, buses, and trucks. This application does not impose any special limitations on the specific form of the vehicle.

[0036] The following is an illustrative diagram illustrating an application scenario of a battery range prediction method. For example, Figure 1 The diagram illustrates an application scenario of the battery range prediction method provided in an exemplary embodiment of this application. Figure 1 As shown, assume that the driver is driving vehicle 100 from location 1 in city A to location 2 in city B, which is 300 kilometers away. Along the way, the driver needs to pass through a mountain road that is about 50 kilometers long.

[0037] Vehicle 100 is an electric vehicle. It starts with a full charge and the destination is set to location 2 in City B on the navigation application. The process of the driver driving vehicle 100 from location 1 in City A to location 2 in City B, which is 300 kilometers away, is as follows: For example, at a certain moment 1 on a highway, vehicle 100 acquires the vehicle's state parameters 1, the road condition parameters 1 for the vehicle's future driving path, and the remaining energy 1; where the state parameters are parameters that affect the vehicle's tire rolling resistance; based on the state parameters 1, road condition parameters 1, and remaining energy 1, the remaining driving range is predicted to be 320 kilometers. If the remaining range is 300 kilometers, the remaining driving range is greater than the remaining range, then the remaining driving range meets the remaining range condition.

[0038] At a certain moment 2 on a mountain road, after vehicle 100 has traveled 150 kilometers, its remaining battery power decreases. Vehicle 100 continuously performs dynamic prediction of the remaining driving range. Vehicle 100 obtains the vehicle's status parameters 2, the road condition parameters 2 of the vehicle's future driving path, and the remaining energy 2. Based on the status parameters 2, road condition parameters 2, and remaining energy 2, the predicted remaining driving range is 140 kilometers. The remaining driving range is dynamically updated to 140 kilometers. The actual remaining range is 150 kilometers. If the remaining driving range is less than the remaining distance, then the remaining driving range does not meet the remaining distance condition, and the first interface is displayed. The first interface includes a prompt message to the user that the vehicle cannot reach its destination using the current driving energy.

[0039] It should be understood that the above application scenario examples are only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited thereto. Rather, the embodiments of this application can be applied to any applicable scenario.

[0040] Exemplary methods Figure 2 The diagram shown is a flowchart illustrating an exemplary embodiment of the battery life prediction method provided in this application. Figure 2 The method can be performed by a vehicle, such as Figure 1 Vehicle 100 is executed. For example... Figure 2 As shown, the range prediction method may include the following steps: 210: Obtain vehicle status parameters, road condition parameters for the vehicle's future driving path, and remaining energy.

[0041] The vehicle can be a pure gasoline vehicle, a pure electric vehicle, or a hybrid vehicle. Therefore, for a pure electric vehicle, the driving range can be measured by the amount of electricity; for a pure gasoline vehicle, the driving range can be measured by the amount of fuel; and for a hybrid vehicle, the driving range can be measured by both the amount of fuel and the amount of electricity. In some embodiments, the remaining fuel level can be obtained through a fuel level sensor, and the remaining battery level can be obtained through a battery level sensor.

[0042] State parameters are those that affect the rolling resistance of a vehicle's tires. These parameters can include tire pressure and temperature. Road condition parameters can include gradient information, curve information, and congestion information. Gradient information can include longitudinal slope angle and rate of change of height; the longitudinal slope angle is the angle between the road surface and the horizontal plane, and the rate of change of height is the change in elevation per unit distance. Curve information can include road curvature and curve length. Congestion information includes traffic flow density and traffic volume; traffic flow density is the number of vehicles per unit road length and the number of vehicles passing through per unit time.

[0043] In some embodiments, a tire pressure monitoring system (TPMS) can monitor tire pressure. Besides monitoring tire pressure, TPMS can indirectly estimate vehicle load and tire temperature, and analyze tire wear trends and road surface roughness by combining wheel speed signals. Road condition parameters can be obtained through navigation applications.

[0044] 220: Predict remaining driving range based on state parameters, road condition parameters, and remaining energy.

[0045] In some embodiments, a vehicle can predict its average energy consumption along a future driving path based on state parameters and road condition parameters; then, based on the remaining driving range and average energy consumption, it can determine the vehicle's remaining driving range. The specific scheme for predicting average energy consumption along a future driving path based on state parameters and road condition parameters will be described in more detail below, and will not be repeated here. This future driving path is the route the vehicle takes from its current location to its destination.

[0046] Average energy consumption refers to the energy consumed per unit of driving distance. Remaining driving range refers to the distance a vehicle can travel using its current driving energy.

[0047] State parameters and road condition parameters change during vehicle operation. If the instantaneous average energy consumption generated based on the current state parameters and road condition parameters is high or low, it may cause inaccurate average energy consumption prediction, which in turn leads to inaccurate prediction of the remaining driving range.

[0048] For example, the vehicle initially travels on a mountain road, which is rugged and consumes a lot of energy. Based on the current state parameters and road condition parameters, the vehicle predicts the average energy consumption. Then, based on the remaining driving range and the aforementioned average energy consumption, the vehicle predicts that the remaining driving range is relatively small. However, in reality, the vehicle is only consuming a lot of energy at the moment, and the subsequent driving is on highways with less energy consumption. In fact, the vehicle can reach its destination.

[0049] Therefore, vehicles can predict average energy consumption on future driving routes based on state parameters and road condition parameters; then, based on remaining energy and average energy consumption, the remaining driving range of the vehicle can be determined. In this way, the state parameters and road condition parameters of the entire route can be comprehensively considered, avoiding the generation of instantaneous average energy consumption based on instantaneous state parameters and road condition parameters, thus improving the accuracy of average energy consumption prediction.

[0050] In some embodiments, the vehicle periodically performs steps 210 to 220 to periodically re-predict the remaining driving range based on changing state parameters, road condition parameters, and remaining energy.

[0051] In this embodiment of the application, real-time physical conditions (such as vehicle load and tire condition) and future road resistance (such as slope and curve) that affect vehicle energy consumption are used as parameters affecting the predicted driving range. This allows for a more accurate simulation of the vehicle's actual driving energy consumption, significantly improving the accuracy of the driving range prediction. The predicted driving range is based on this higher accuracy.

[0052] The following section details a specific scheme for predicting average energy consumption on future driving routes based on state parameters and road condition parameters.

[0053] Road conditions change as a vehicle travels, and average energy consumption can be predicted based on these changes, improving the accuracy of average energy consumption prediction. Specifically, in some embodiments, this can be achieved through steps 301 to 305 as follows: 301: Divide the future driving route into a preset number of road segments.

[0054] It is understandable that the preset road segment can be divided according to the road type, such as highway, expressway, mountain road, country road, etc., but is not limited to this.

[0055] 302: Determine the road condition parameters corresponding to each road segment in the preset number of road segments.

[0056] The road condition parameters corresponding to each road segment can be the average value of periodically collected road condition parameters, but are not limited to this.

[0057] 303: Based on state parameters and road condition parameters corresponding to each road segment, predict the energy consumption of each road segment.

[0058] Based on state parameters and road condition parameters corresponding to each road segment, the specific implementation scheme for predicting the energy consumption of each road segment will be described in detail below.

[0059] 304: Determine the total energy consumption of future travel routes based on the energy consumption of each road segment.

[0060] 305: Based on total energy consumption and the path distance corresponding to the future driving path, predict the average energy consumption on the future driving path.

[0061] In one embodiment, the total energy consumption can be divided by the path distance corresponding to the future driving path to obtain the average energy consumption on the future driving path.

[0062] In this embodiment, the continuous and complex future driving path is divided into preset road segments, and the road condition parameters of each road segment are determined. Combined with the current state parameters of the vehicle and the road condition parameters of the road segment, the energy consumption of each road segment is predicted. This largely restores the real driving scenario and can accurately predict the energy consumption of each road segment, thereby accurately predicting the average energy consumption on the future driving path.

[0063] The specific implementation scheme of the aforementioned 303 steps will be introduced next.

[0064] In some embodiments, state parameters and road condition parameters corresponding to each road segment can be input into the power consumption prediction model, and the power consumption prediction model can be used to predict the energy consumption of each road segment.

[0065] In this embodiment, by combining state parameters reflecting the real-time operating status of the vehicle with road condition parameters reflecting the characteristics of the external environment, the energy consumption prediction model is made adaptive to the actual operating conditions, which significantly improves the accuracy and reliability of energy consumption prediction and provides accurate data support for range replenishment reminders.

[0066] It is understandable that by constructing a three-level cascaded correction mechanism consisting of load correction coefficient, tire condition correction coefficient, and road condition correction coefficient, the basic energy consumption prediction model is dynamically corrected in multiple dimensions based on vehicle dynamics. This effectively eliminates prediction errors caused by fixed parameter assumptions while ensuring computational efficiency, significantly improving the accuracy of energy consumption prediction under complex operating conditions. Based on this, in some embodiments, the state parameters include the tire pressure and temperature of the vehicle's tires. The energy consumption prediction model predicts the energy consumption of each road segment based on the aforementioned state parameters and road condition parameters, including the following steps 30301 to 30304: 30301: The power consumption prediction model is used to determine the load correction factor based on tire pressure; wherein, the load correction factor is used to adjust the vehicle's preset load parameters in the power consumption prediction model, and the load correction factor is positively correlated with the average energy consumption.

[0067] The positive correlation between load correction factor and average energy consumption means that the larger the load correction factor, the greater the average energy consumption.

[0068] 30302: The power consumption prediction model is used to determine the tire condition correction coefficient based on tire pressure and temperature; wherein, the tire condition correction coefficient is used to adjust the rolling resistance coefficient of the tire in the power consumption prediction model, and the tire condition correction coefficient is positively correlated with the average energy consumption.

[0069] The positive correlation between tire condition correction factor and average energy consumption means that the larger the tire condition correction factor, the greater the average energy consumption.

[0070] 30303: The power consumption prediction model is used to determine the road condition correction coefficient based on road condition parameters; wherein, the road condition correction coefficient is used to adjust the preset road condition coefficient in the power consumption prediction model, and the road condition correction coefficient is positively correlated with the average energy consumption.

[0071] The positive correlation between road condition correction factor and average energy consumption means that the larger the road condition correction factor, the greater the average energy consumption.

[0072] 30304: The power consumption prediction model is used to predict the energy consumption of each road segment based on load correction factors, tire condition correction factors, and road condition correction factors.

[0073] The aforementioned three-level cascaded correction mechanism, which constructs a load correction coefficient, a tire condition correction coefficient, and a road condition correction coefficient, is based on vehicle dynamics formulas and takes into account the potential energy changes required to overcome the gradient, the additional resistance of cornering, and the wind resistance and mechanical losses in different speed ranges.

[0074] A specific application of the aforementioned correction coefficients in power consumption prediction models: The load correction factor (K_load) is mainly used to adjust vehicle mass parameters. The larger the load correction factor, the higher the energy consumption under the same road conditions.

[0075] When calculating the acceleration drag and gradient drag for each segment, the calibrated mass is multiplied by (1 + K_load) to reflect the impact of the actual load on average energy consumption. The calibrated mass is the standard mass of the vehicle under no-load / full-load conditions. For example, K_load = (current load - calibrated mass) / calibrated mass; The tire condition correction factor (K_tire) is mainly used to adjust the rolling resistance coefficient. Insufficient tire pressure, excessive temperature, or severe wear will increase K_tire, thus increasing rolling resistance energy consumption. The rolling resistance coefficient in the rolling resistance calculation formula is multiplied by (1 + K_tire).

[0076] The road condition correction factor (K_road) is a comprehensive road segment multiplicative factor. For example, it is determined by the slope, curvature of curves, and predicted average vehicle speed of the road segment, resulting in a comprehensive difficulty coefficient. This coefficient is directly multiplied by the preset road condition coefficient for that road segment, thereby quantifying the amplification effect of complex road conditions on energy consumption. The preset load parameters are the baseline energy consumption per unit distance when the vehicle travels at a constant speed under ideal road conditions: flat, dry, no slope, no curves, and no congestion.

[0077] In some embodiments, based on the aforementioned predicted remaining driving range, it can be predicted whether the vehicle can reach its destination. If it is predicted that the vehicle cannot reach its destination, a prompt message can be output to inform the user that the vehicle cannot reach its destination. For example, the prompt message could be "Based on the current energy and road conditions ahead, it is recommended to recharge in advance to ensure reaching the destination."

[0078] The prompts can be delivered via voice or by displaying the prompts, but are not limited to these methods.

[0079] Furthermore, in some embodiments, prompt information can be displayed on the central control display screen, head-up display (HUD), or head-up touchscreen (HUT).

[0080] Based on the aforementioned predicted remaining driving range, a specific implementation plan for predicting whether a vehicle can reach its destination can be based on the remaining driving range and remaining driving range conditions, including the following two methods, but not limited to these: Method 1 In some embodiments, if the remaining driving range is less than the target mileage, it is determined that the remaining driving range does not meet the remaining mileage condition; wherein the target mileage is the distance from the vehicle's current location to its destination; if the remaining driving range is greater than or equal to the target mileage, it is predicted that the remaining driving range meets the remaining mileage condition.

[0081] Method 2 If the remaining driving range is less than the sum of the target range and the preset range, it is determined that the remaining driving range does not meet the remaining driving range condition; if the remaining driving range is greater than or equal to the sum of the target range and the preset range, it is predicted that the remaining driving range meets the remaining driving range condition.

[0082] For example, if the remaining driving range is 140 km, the remaining range is 150 km, the preset range (safety redundancy range) is 30 km, and the sum of the remaining range and the preset range is 180 km, then 140 km is less than 180 km, and the predicted remaining driving range meets the remaining range condition.

[0083] In this application, the preset mileage is a safety redundancy, and the vehicle has a built-in safety redundancy mileage judgment, providing a buffer space for finding charging points and avoiding the risk of breakdown due to prediction errors or unexpected situations. Furthermore, based on highly accurate predicted driving range, it can accurately predict situations where the remaining driving range is less than the sum of the target mileage and the preset mileage, promptly reminding the user and improving the reliability of driving range reminders.

[0084] If the remaining driving range does not meet the remaining range requirement, the charging point recommendation logic is triggered. For example, in some embodiments, the vehicle periodically predicts the remaining driving range, and if the remaining driving range is less than the target range, the charging point recommendation logic is triggered. In other embodiments, the vehicle periodically predicts the remaining driving range, and if the remaining driving range is less than the sum of the target range and a preset range, the charging point recommendation logic is triggered.

[0085] Specifically, the energy replenishment point recommendation logic may include the following steps 401 to 404: 401: Determine the target refueling point set, which includes multiple target refueling points that the vehicle can reach using its remaining range energy.

[0086] In some embodiments, the vehicle can use its current location as the starting point and the remaining driving range as the radius to search for charging points in the charging point database provided by the navigation map that match the aforementioned starting point and radius. Then, the dynamic energy consumption model is used to calculate the energy consumption required for the vehicle to travel from the current location to each candidate charging point. If the energy consumption is less than the remaining driving range, the charging point is taken as the target charging point that can be reached using the remaining driving range. Multiple target charging points form a target charging point set.

[0087] 402: Determine the charging points that match the preset user preference settings from the target charging point set.

[0088] The preset user preference settings can be the shortest total time, the shortest detour distance, and the best cost (lowest energy price). The aforementioned total time is the sum of the time it takes for the vehicle to travel to the refueling point, the vehicle's refueling time, and the time it takes for the vehicle to travel from the refueling point to its destination.

[0089] The following describes how to determine the refueling point that has the shortest total time, the shortest detour distance, and the best cost.

[0090] The method for determining the energy replenishment point with the shortest total time includes the following: The vehicle can first determine a set of time intervals; where each time interval in the set is the sum of the time it takes for the vehicle to travel to the refueling point, the time it takes to refuel, and the time it takes to travel from the refueling point to its destination. Then, the vehicle selects the refueling point with the shortest time interval from the set of time intervals as its first refueling point.

[0091] The method for determining the refueling point corresponding to the minimum detour distance includes the following steps: The vehicle can first determine a set of path lengths; each path length in the set represents the path length from which the vehicle travels to the refueling point. Then, the refueling point with the shortest path length in the set is selected as the second refueling point. In this way, the refueling point that is closest to the original planned path and least deviates from the main path can be chosen.

[0092] The method for determining the optimal cost includes the following steps: The vehicle can first determine the set of energy prices; where each energy price in the set of energy prices is the energy price of each refueling point in the target refueling point set. The refueling point with the lowest energy price from the set of energy prices is then selected as the third refueling point. The energy price can be electricity or fuel.

[0093] In some embodiments, the vehicle's central control display screen may show user preference settings options, and the vehicle may determine preset user preference settings in response to the user's selection of user preference settings options. For example, user preference settings options may include the option with the shortest total time, the option with the shortest detour distance, and the option with the best cost, and the user may choose one or more of the aforementioned three options.

[0094] 403: Displays recommended energy replenishment points that match the preset user preference settings.

[0095] The power replenishment points that conform to the preset user preference settings can include any combination of the first power replenishment point, the second power replenishment point, and the third power replenishment point.

[0096] Recommended information for charging points that match preset user preferences includes detailed data such as the charging point name, estimated remaining battery life upon arrival, recommended amount of charging, and estimated arrival time at the destination after charging, but is not limited to these.

[0097] In some embodiments, recommended charging points that conform to preset user preference settings can be displayed in the form of a floating window.

[0098] Furthermore, in some embodiments, recommended information about charging points that conform to preset user preference settings can be displayed on the central control display screen, head-up display, or head-up touch system.

[0099] In this embodiment, recommended information about energy replenishment points that conform to preset user preferences can be displayed to improve the user experience.

[0100] To improve the visualization of remaining driving range and enhance the user's visual alert experience, a first marker ring, a set of controls, or a first marker ring and a set of spaces can be displayed on the navigation map. The first marker ring is a circle with the vehicle's location as the center and the remaining driving range as the radius. Each control in the control set is a control located on the navigation map at the location of a charging point that conforms to the preset user preference settings. The control is used to respond to the user's preset operation and uses the charging point corresponding to the control as a navigation waypoint.

[0101] For example, the first marker ring could be a semi-transparent, gradient-colored (e.g., from green to yellow) visual range centered on the location of the vehicle icon on the navigation map, with the remaining driving range as its radius. Near the edge of the first marker ring or outside the ring, each control in the control set is highlighted.

[0102] Understandably, the first marker ring can change dynamically during vehicle operation, intuitively reflecting the real-time nature of the remaining driving range prediction, making it easier for drivers to understand and trust the system's prediction results.

[0103] For example, Figure 3 The diagram shown is a schematic representation of a first interface provided in an exemplary embodiment of this application. Figure 3 As shown, interface 3033 includes vehicle 100, prompt information 30331, a first marker ring 30332, controls corresponding to charging points 30333, and recommended charging point information 30334 that conforms to preset user preferences. Vehicle 100's current location is location 1 in city A, and its destination is location 2 in city B. For example, prompt information 30331 states: "Based on current energy and road conditions ahead, it is recommended to charge in advance to ensure arrival at the destination." Recommended charging point information 30334 that conforms to preset user preferences states: "Charging point AA, the remaining range at this charging point is 30 mAh, it is recommended to charge for 2 hours, and the estimated arrival time at the destination after charging is 30 minutes."

[0104] In this application, the first marker ring, namely the "dynamic range ring," is a visual design that intuitively displays all geographical areas the vehicle can currently reach. It transforms abstract range figures into a clear image of the geographical area, allowing the driver to immediately see the extent of their current energy range, greatly improving the user-friendliness of human-machine interaction and the ability to perceive driving situations. Users can easily set it as a navigation waypoint or new destination with a single touch, enhancing the user experience.

[0105] Figure 4 The diagram shown is a structural schematic of a vehicle provided in an exemplary embodiment of this application. Figure 4As shown, the vehicle 100 includes a vehicle control unit (VCU) 101, a tire pressure sensor 102, a fuel level sensor 103, a battery level sensor 104, an odometer 105, and a central control display screen 106.

[0106] The vehicle controller 101 is used to execute the range prediction method provided in the embodiments of this application. Specifically, it acquires the vehicle's state parameters, road condition parameters of the vehicle's future driving path, and remaining energy. The state parameters are parameters that affect the vehicle's tire rolling resistance. The remaining driving range is predicted based on the state parameters, road condition parameters, and remaining energy.

[0107] The technical details of the vehicle controller 101 for executing the range prediction method provided in the embodiments of this application are described above and will not be repeated here.

[0108] In some embodiments, the vehicle controller 101 can obtain tire pressure from the tire pressure sensor 102, fuel level from the fuel level sensor 103, battery level from the battery level sensor 104, and remaining mileage from the odometer 105. It can also display prompt information, recommended charging point information that conforms to the preset user preference settings, and a first marker ring and / or control set marked on the navigation map on the central control display screen 106. The detailed information of the aforementioned prompt information, recommended charging point information, first marker ring and / or control set has been described above and will not be repeated here.

[0109] Exemplary device Figure 5 The diagram shown is a schematic representation of the battery life prediction device provided in an exemplary embodiment of this application. Figure 5 As shown, the range prediction device 500 includes an acquisition module 510 and a prediction module 520.

[0110] The acquisition module 510 is used to acquire the vehicle's state parameters, road condition parameters for the vehicle's future driving path, and remaining energy; wherein the state parameters are parameters that affect the vehicle's tire rolling resistance; the prediction module 520 is used to predict the remaining driving range based on the state parameters, road condition parameters, and remaining energy.

[0111] This application provides a range prediction device that uses real-time physical conditions (such as vehicle load and tire condition) and future road resistance (such as slope and curves) that affect vehicle energy consumption as parameters for predicting range, thereby enabling more accurate simulation of the vehicle's actual driving energy consumption and significantly improving the accuracy of range prediction.

[0112] According to one embodiment of this application, the prediction module 520 is used to predict the average energy consumption on a future driving path based on state parameters and road condition parameters; wherein the future driving path is the path of the vehicle from its current location to its destination; and to determine the remaining driving range of the vehicle based on the remaining driving energy and the average energy consumption.

[0113] According to one embodiment of this application, the prediction module 520 is used to divide the future driving path into a preset number of road segments; determine the road condition parameters corresponding to each road segment in the preset number of road segments; predict the energy consumption of each road segment based on the state parameters and the road condition parameters corresponding to each road segment; determine the total energy consumption of the future driving path based on the energy consumption of each road segment; and predict the average energy consumption on the future driving path based on the total energy consumption and the path distance corresponding to the future driving path.

[0114] According to one embodiment of this application, the prediction module 520 is used to input the state parameters and the road condition parameters corresponding to each road segment into the power consumption prediction model, and use the power consumption prediction model to predict the energy consumption of each road segment.

[0115] According to one embodiment of this application, the prediction module 520 is used for a power consumption prediction model to determine a load correction coefficient based on tire pressure; wherein the load correction coefficient is used to adjust the preset load parameters of the vehicle in the power consumption prediction model, and the load correction coefficient is positively correlated with the average energy consumption; the power consumption prediction model is used to determine a tire condition correction coefficient based on tire pressure and temperature; wherein the tire condition correction coefficient is used to adjust the rolling resistance coefficient of the tire in the power consumption prediction model, and the tire condition correction coefficient is positively correlated with the average energy consumption; the power consumption prediction model is used to determine a road condition correction coefficient based on road condition parameters; wherein the road condition correction coefficient is used to adjust the preset road condition coefficient in the power consumption prediction model, and the road condition correction coefficient is positively correlated with the average energy consumption; the power consumption prediction model is used to predict the energy consumption of each road segment based on the load correction coefficient, the tire condition correction coefficient, and the road condition correction coefficient.

[0116] According to one embodiment of this application, the range prediction device 500 may further include an output module 530, which is used to output a prompt message when the remaining range does not meet the remaining range condition; wherein, the target range is the distance from the vehicle's current location to the destination, and the prompt message is used to remind the user that the vehicle cannot reach the destination using the current range energy.

[0117] According to one embodiment of this application, the prediction module 520 is further configured to determine a target refueling point set, the target refueling point set including multiple target refueling points, which are refueling points that the vehicle can reach using its remaining range energy; determine refueling points that conform to preset user preference settings from the target refueling point set; the output module 530 is further configured to display recommendation information of refueling points that conform to preset user preference settings; wherein, refueling points that conform to preset user preference settings include any one or more of first refueling point information, second refueling point information, and third refueling point information; the first refueling point information includes a first refueling point, the second refueling point information includes a second refueling point, and the third refueling point information includes a third refueling point; the first refueling point is the refueling point corresponding to the shortest time in the target refueling point set; the second refueling point is the second refueling point closest to the vehicle's current location; and the third refueling point is the refueling point with the lowest energy price in the target refueling point set.

[0118] According to one embodiment of this application, the output module 530 is further configured to display a first marker ring and / or a set of controls marked on a navigation map; wherein, the first marker ring is a ring with the vehicle's location as the center and the remaining driving range as the radius; each control in the control set is a control located on the navigation map at the location of a charging point that conforms to a preset user preference setting, and the control is used to respond to the user's preset operation and use the charging point corresponding to the control as a navigation waypoint or a new destination.

[0119] This application also provides a computer-readable storage medium storing a computer program for executing the battery life prediction method provided in any of the above embodiments.

[0120] This application also provides a computer program product, which includes a computer program. When the computer program is executed by the processor of a computer device, it enables the computer device to execute the battery life prediction method provided in any of the above embodiments.

[0121] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and will not be described in detail here.

[0122] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0123] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0125] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] In addition, the functional units in the various embodiments of this application 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.

[0127] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program verification codes, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] It should be noted that in the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0130] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting battery life, characterized in that, The method includes: The vehicle's state parameters, road condition parameters for the vehicle's future driving path, and remaining energy are obtained; wherein, the state parameters are parameters that affect the vehicle's tire rolling resistance. The remaining driving range is predicted based on the state parameters, the road condition parameters, and the remaining energy.

2. The range prediction method according to claim 1, characterized in that, The method of predicting the remaining driving range based on the state parameters, the road condition parameters, and the remaining driving energy includes: Based on the state parameters and the road condition parameters, the average energy consumption on the future driving path is predicted; wherein, the future driving path is the path taken by the vehicle from its current location to its destination; The remaining driving range of the vehicle is determined based on the remaining driving energy and the average energy consumption.

3. The range prediction method according to claim 2, characterized in that, The prediction of average energy consumption on the future travel path based on the state parameters and the road condition parameters includes: The future driving path is divided into a preset number of road segments; Determine the road condition parameters corresponding to each of the preset road segments; Based on the state parameters and the road condition parameters corresponding to each road segment, the energy consumption of each road segment is predicted; The total energy consumption of the future travel route is determined based on the energy consumption of each road segment. Based on the total energy consumption and the path distance corresponding to the future driving path, the average energy consumption on the future driving path is predicted.

4. The range prediction method according to claim 3, characterized in that, The prediction of energy consumption for each road segment based on the state parameters and the road condition parameters corresponding to each road segment includes: The state parameters and the road condition parameters corresponding to each road segment are input into the power consumption prediction model, and the power consumption prediction model is used to predict the energy consumption of each road segment.

5. The range prediction method according to claim 4, characterized in that, The state parameters include the tire pressure and temperature of the vehicle's tires, and the prediction of the average energy consumption on the future driving path using the power consumption prediction model includes: The power consumption prediction model is used to determine the load correction coefficient based on the tire pressure; wherein, the load correction coefficient is used to adjust the preset load parameters of the vehicle in the power consumption prediction model, and the load correction coefficient is positively correlated with the average energy consumption; The power consumption prediction model is used to determine the tire condition correction coefficient based on the tire pressure and the temperature; wherein, the tire condition correction coefficient is used to adjust the rolling resistance coefficient of the tire in the power consumption prediction model, and the tire condition correction coefficient is positively correlated with the average energy consumption; The power consumption prediction model is used to determine the road condition correction coefficient based on the road condition parameters; wherein, the road condition correction coefficient is used to adjust the preset road condition coefficient in the power consumption prediction model, and the road condition correction coefficient is positively correlated with the average energy consumption; The power consumption prediction model is used to predict the energy consumption of each road segment based on the load correction coefficient, the tire condition correction coefficient, and the road condition correction coefficient.

6. The range prediction method according to claim 2, characterized in that, The method further includes: If the remaining driving range does not meet the remaining range condition, a prompt message is output; wherein, the target mileage is the distance from the vehicle's current location to the destination, and the prompt message is used to inform the user that the vehicle cannot reach the destination using the current driving range.

7. The range prediction method according to claim 6, characterized in that, The method further includes: Determine a set of target refueling points, the set of target refueling points including multiple target refueling points, the multiple target refueling points being refueling points that the vehicle can reach using its remaining driving energy; Determine charging points that match the preset user preference settings from the target charging point set; The system displays recommended charging points that conform to the preset user preference settings. The charging points that conform to the preset user preference settings include any one or more of the following: first charging point information, second charging point information, and third charging point information. The first charging point information includes a first charging point, the second charging point information includes a second charging point, and the third charging point information includes a third charging point. The first charging point is the charging point with the shortest time consumption in the target charging point set. The second charging point is the second charging point closest to the vehicle's current location. The third charging point is the charging point with the lowest energy price in the target charging point set.

8. The range prediction method according to claim 7, characterized in that, The method further includes: displaying a first marker ring and / or a set of controls marked on a navigation map; Wherein, the first marking ring is a ring with the vehicle's location as the center and the remaining driving range as the radius; each control in the control set is a control located on the navigation map at the location of the information of the charging point that conforms to the preset user preference settings, and the control is used to respond to the user's preset operation and use the charging point corresponding to the control as a navigation waypoint or a new destination.

9. A vehicle, characterized in that, include: In-vehicle infotainment system; Memory used to store executable instructions of the vehicle system. The vehicle-mounted system is used to execute the range prediction method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for executing the range prediction method according to any one of claims 1 to 8.

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