A vehicle energy consumption prediction method, electronic device and vehicle

Abstract

The application discloses a vehicle energy consumption prediction method, an electronic device and a vehicle, and relates to the technical field of intelligent driving. The method obtains the section length and the section reference speed of each to-be-traveled section in a to-be-traveled path of a vehicle. For each to-be-traveled section, the predicted speed and the predicted acceleration of the vehicle on the to-be-traveled section are determined based on the section length and the section reference speed of the to-be-traveled section. The predicted driving power of the vehicle on each to-be-traveled section is calculated based on the predicted speed and the predicted acceleration of the vehicle on each to-be-traveled section. The unit mileage energy consumption of the vehicle on the to-be-traveled path is predicted based on the predicted driving power of the vehicle on each to-be-traveled section and the section length of each to-be-traveled section. The energy consumption level of the vehicle on the actual to-be-traveled path can be more accurately reflected, and the accuracy of the predicted unit mileage energy consumption is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and in particular to a vehicle energy consumption prediction method, electronic device, and vehicle. Background Technology

[0002] As the level of automotive intelligence continues to improve, vehicle energy management technology is receiving increasing attention. Energy consumption on the planned route is a key prerequisite for optimizing energy distribution and improving energy efficiency, directly impacting users' travel planning and refueling arrangements. Therefore, predicting energy consumption per unit mile on the planned route is particularly important.

[0003] Currently, the unit mileage of a vehicle on the route to be traveled is usually predicted based on historical driving data of the road sections already traveled, or by directly using the unit mileage of a vehicle on the route to be traveled under preset standard operating conditions.

[0004] However, in the case of complex and variable actual driving conditions, the historical driving conditions and the preset standard conditions may deviate significantly from the actual driving conditions of the vehicle on the route to be driven, which can easily lead to a large deviation in the prediction of energy consumption per unit mileage, resulting in poor accuracy of the final predicted energy consumption per unit mileage of the vehicle on the route to be driven. Summary of the Invention

[0005] In view of the above problems, this application provides a vehicle energy consumption prediction method, electronic device, and vehicle to improve the accuracy of predicting energy consumption per unit mileage. The specific solution is as follows:

[0006] The first aspect of this application provides a method for predicting vehicle energy consumption, including:

[0007] Obtain the length of each road segment and the base speed of each segment in the vehicle's planned route.

[0008] For each road segment to be driven, based on the road segment length and the road segment reference speed, the predicted speed and predicted acceleration of the vehicle in the road segment to be driven are determined;

[0009] Based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be driven, the predicted driving power of the vehicle in each of the road segments to be driven is calculated.

[0010] Based on the predicted driving power of the vehicle in each of the road segments to be driven and the length of each road segment to be driven, the energy consumption per unit mile of the vehicle on the road to be driven is predicted.

[0011] In one possible implementation, calculating the predicted driving power of the vehicle in each of the road segments to be driven, based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be driven, includes:

[0012] Based on the predicted speed of the vehicle in each of the road segments to be driven, the sliding resistance of the vehicle in each of the road segments to be driven is calculated.

[0013] Based on the obtained total mass of the vehicle and the predicted acceleration of the vehicle in each of the road segments to be driven, the acceleration resistance of the vehicle in each of the road segments to be driven is calculated.

[0014] Based on the total mass of the vehicle and the slope of each road segment to be driven, the slope resistance of the vehicle on each road segment to be driven is calculated.

[0015] Based on the vehicle's coasting resistance, acceleration resistance, and gradient resistance in each of the road segments to be driven, as well as the vehicle's predicted speed in each of the road segments to be driven, the predicted driving power of the vehicle in each of the road segments to be driven is calculated.

[0016] In one possible implementation, after calculating the predicted driving power of the vehicle for each of the road segments to be traveled based on the predicted speed and predicted acceleration of the vehicle for each of the road segments to be traveled, the method further includes:

[0017] Based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be driven, the braking energy recovery of the vehicle in each of the road segments to be driven is determined.

[0018] Based on the regenerative braking energy of the vehicle in each of the road segments to be driven, the predicted driving power of the vehicle in each of the road segments to be driven is corrected to obtain the first corrected driving power of the vehicle in each of the road segments to be driven.

[0019] The predicted driving power of the vehicle in each of the road segments to be driven is updated to the first corrected driving power of the vehicle in each of the road segments to be driven.

[0020] In one possible implementation, after calculating the predicted driving power of the vehicle for each of the road segments to be traveled based on the predicted speed and predicted acceleration of the vehicle for each of the road segments to be traveled, the method further includes:

[0021] Based on the historical predicted driving power and historical actual driving power of the vehicle in each of the road segments to be driven, the driving power correction coefficient of the vehicle is calculated.

[0022] Based on the vehicle's drive power correction coefficient, the predicted drive power of the vehicle in each of the road segments to be driven is corrected to obtain the second corrected drive power of the vehicle in each of the road segments to be driven.

[0023] The predicted driving power of the vehicle in each of the road segments to be driven is updated to the second corrected driving power of the vehicle in each of the road segments to be driven.

[0024] In one possible implementation, determining the predicted speed and predicted acceleration of the vehicle on each of the road segments to be driven, based on the segment length and the reference speed of the segment, includes:

[0025] Based on the preset interpolation distance and the length of the road segment to be driven, the road segment to be driven is interpolated and broken to obtain multiple interpolation points;

[0026] Multiple data points are constructed using the distance between each interpolation point and the starting point of the road segment to be driven as the abscissa and the road segment reference speed as the ordinate.

[0027] By fitting the multiple data points, a predicted speed function of the vehicle on the road segment to be driven is obtained, and based on the predicted speed function of the vehicle on the road segment to be driven, the predicted speed of the vehicle on the road segment to be driven is obtained.

[0028] Differentiate the predicted speed function of the vehicle on the road segment to be driven to obtain the acceleration function of the vehicle on the road segment to be driven, and obtain the predicted acceleration of the vehicle on the road segment to be driven based on the acceleration function of the vehicle on the road segment to be driven.

[0029] In one possible implementation, predicting the energy consumption per unit mile of the vehicle on the planned travel path based on the predicted driving power of the vehicle in each of the planned travel segments and the length of each planned travel segment includes:

[0030] The predicted driving power of the vehicle in each of the road segments to be driven is integrated along the length of each road segment to obtain the total mechanical energy of the vehicle on the road to be driven.

[0031] The total mechanical energy of the vehicle on the driving path is converted to obtain the total battery energy consumption of the vehicle on the driving path.

[0032] Based on the length of each road segment to be driven, the total length of the route to be driven is calculated, and based on the total battery energy consumption of the vehicle on the route to be driven and the total length of the route to be driven, the energy consumption per unit mile of the vehicle on the route to be driven is predicted.

[0033] In one possible implementation, after predicting the vehicle's energy consumption per unit mile on the planned route based on the vehicle's predicted drive power in each of the planned road segments and the length of each planned road segment, the method further includes:

[0034] Based on the remaining battery energy of the vehicle and the energy consumption per unit mile of the vehicle on the route to be traveled, the driving range of the vehicle is predicted.

[0035] Based on the obtained vehicle operating conditions, the vehicle's remaining driving range is displayed.

[0036] In one possible implementation, after displaying the vehicle's remaining driving range based on the acquired vehicle operating conditions, the method further includes:

[0037] Calculate the absolute value of the difference between the currently displayed driving range of the vehicle and the previously displayed driving range of the vehicle;

[0038] Determine whether the absolute value of the difference is greater than a preset value. If the absolute value of the difference is greater than the preset value, generate a prompt message and display the prompt message, which is used to indicate the change in the vehicle's remaining driving range.

[0039] A second aspect of this application provides an electronic device, including at least one processor and a memory connected to the processor, wherein:

[0040] The memory is used to store computer programs;

[0041] The processor is used to execute the computer program so that the electronic device can implement the vehicle energy consumption prediction method of the first aspect or any implementation thereof.

[0042] A third aspect of this application provides a computer storage medium carrying one or more computer programs, which, when executed by an electronic device, enable the electronic device to implement the vehicle energy consumption prediction method of the first aspect or any implementation thereof.

[0043] The fourth aspect of this application provides a vehicle including an electronic device that implements the first aspect or any implementation thereof.

[0044] By employing the above technical solutions, this application provides a vehicle energy consumption prediction method, electronic device, and vehicle. This method obtains the length of each road segment and the reference speed of that segment in the vehicle's planned driving path, enabling the acquisition of the actual road conditions the vehicle will be traveling on. For each road segment, based on the segment length and reference speed, the predicted speed and acceleration of the vehicle in that segment are determined, making the predicted driving state closer to the vehicle's actual driving situation on that segment. Based on the predicted speed and acceleration of the vehicle in each road segment, the predicted driving power of the vehicle in each road segment is calculated, accurately reflecting the actual power output required by the vehicle on different road segments. Based on the predicted driving power and length of each road segment, the energy consumption per unit mile of the vehicle on the planned driving path is predicted. This more accurately reflects the energy consumption level of the vehicle on the actual planned driving path, improving the accuracy of the predicted energy consumption per unit mile. Attached Figure Description

[0045] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0046] Figure 1 A flowchart illustrating a vehicle energy consumption prediction method provided in an embodiment of this application;

[0047] Figure 2 A schematic diagram illustrating a process for updating the predicted driving power of a vehicle in each road segment to be driven, provided in an embodiment of this application;

[0048] Figure 3 A schematic diagram illustrating another process for updating the predicted driving power of a vehicle in each road segment to be driven, provided in an embodiment of this application;

[0049] Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0050] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0051] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0052] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0053] Existing vehicle energy consumption prediction methods typically rely on historical average energy consumption data or fixed operating condition models for estimation. While these methods can provide predicted energy consumption results, they primarily depend on historical statistics or preset operating condition parameters, failing to fully incorporate road segment characteristics such as segment length, baseline vehicle speed, and gradient along the actual driving path. They also struggle to reflect changes in predicted speed, acceleration, and drive power across different road segments. Therefore, in complex and variable real-world road environments, existing methods are prone to significant discrepancies between predicted energy consumption per unit mileage and the actual energy consumption level along the vehicle's intended path. Thus, how to dynamically and accurately predict vehicle speed changes and energy consumption levels along future paths using only real-time traffic information provided by navigation systems, without relying on historical data or fixed operating condition templates, has become a pressing technical challenge in this field.

[0054] To improve the accuracy of predicted energy consumption per unit mile, the core concept of this application is to move beyond simply estimating overall energy consumption based on historical average energy consumption or fixed standard operating conditions. Instead, it divides the driving path into multiple segments and uses the segment length, reference speed, and other characteristics of each segment to predict the vehicle's future driving status on each segment. Specifically, this application first determines the predicted speed and acceleration of the vehicle on each segment based on the segment length and reference speed. Then, it calculates the predicted drive power of the vehicle on each segment based on the predicted speed and acceleration. Finally, it combines the segment length of each segment to predict the energy consumption per unit mile of the entire driving path. Therefore, this application enables the predicted energy consumption per unit mile to simultaneously reflect the segment characteristics of the driving path and the vehicle's future dynamic driving status, thereby reducing prediction deviations caused by inconsistencies between historical or fixed operating conditions and the actual driving path.

[0055] This application provides a vehicle energy consumption prediction method. The following description, in conjunction with the accompanying drawings and specific embodiments, provides a more detailed explanation of the vehicle energy consumption prediction method on the driving path provided in this application.

[0056] Please see the appendix Figure 1 , Figure 1 This is a flowchart illustrating a vehicle energy consumption prediction method provided in an embodiment of this application. The method may include the following steps:

[0057] Step S101: Obtain the length of each road segment and the base speed of each road segment in the vehicle's driving path.

[0058] In this application, the "path to be traveled" refers to the route currently planned by the vehicle's navigation system, which is provided by the navigation system. The "road segment to be traveled" is a basic unit formed by the navigation system after segmenting the path to be traveled, i.e., a road condition column. The length of the road segment and the reference speed of the road segment are obtained by acquiring road condition data from the navigation system on a per-trip basis. Specifically, the road condition data provided by the navigation system includes the road condition status (smooth, slow, congested) and length of the route, the road segment gradient (angle or length-to-height ratio), the road type (national highway, expressway, rapid transit), and the road speed limit. The road segment length corresponds to the length information of each road condition column; the reference speed of the road segment corresponds to the navigation average speed of each road condition column, which is calculated by the navigation system based on the current road condition status (smooth, slow, congested) and the road speed limit.

[0059] The route to be traveled is composed of multiple consecutive road segments connected sequentially, each with independent road segment attribute information. The navigation system breaks down the road ahead according to key locations such as road condition change points, road type change points, and gradient change points, forming multiple discrete road segments to be traveled. Each road segment to be traveled contains the following attribute information: segment length, segment base speed, road condition status, road type, and segment gradient. The route to be traveled can be referred to as a navigation road condition column (path), and the road segments to be traveled can be referred to as road condition columns. The road condition column is the basic unit for describing road segments in the navigation system, containing information such as the road condition status, length, and speed of that segment. By reading the road condition column information provided by the navigation system, the segment length and base speed of each road segment in the route to be traveled can be obtained.

[0060] The reference speed for a road segment is an average driving speed reference value determined by the navigation system based on real-time traffic data and road speed limit information. It is not the actual driving speed of the vehicle on that segment. Specific criteria for determination include: Road conditions: The navigation system classifies road conditions into three levels: smooth, slow, and congested, with different speed attenuation coefficients for each level; Road speed limit: The legally mandated maximum speed limit for that road segment serves as the upper limit constraint for the reference speed; Road type: Different road types, such as national highways, expressways, and expressways, have different reference speed characteristics. For example, for a section of highway to be traveled, if the road conditions are smooth, the reference speed may be close to the highway's speed limit; if the road conditions are congested, the reference speed will be significantly lower than the speed limit, reflecting the actual traffic flow speed.

[0061] The road segment length and baseline vehicle speed obtained in this step are the foundational input data for subsequent speed fitting and acceleration calculations. It should be noted that the road segment length and baseline vehicle speed obtained in this step are predictive data, derived from real-time traffic predictions by the navigation system, rather than the vehicle's historical driving records or preset standard operating condition data.

[0062] Step S102: For each road segment to be driven, based on the length of the road segment and the reference speed of the road segment, determine the predicted speed and predicted acceleration of the vehicle in the road segment to be driven.

[0063] In this application, firstly, based on a preset interpolation distance and the length of the road segment to be driven, the road segment to be driven is interpolated and broken into multiple interpolation points. Then, multiple data points are constructed with the distance between each interpolation point and the starting point of the road segment to be driven as the x-axis and the reference vehicle speed of the road segment to be driven as the y-axis. Next, the multiple data points are fitted to obtain the predicted speed function of the vehicle on the road segment to be driven, and the predicted speed of the vehicle on the road segment to be driven is obtained based on the predicted speed function of the vehicle on the road segment to be driven. Finally, the derivative of the predicted speed function of the vehicle on the road segment to be driven is taken to obtain the acceleration function of the vehicle on the road segment to be driven, and the predicted acceleration of the vehicle on the road segment to be driven is obtained based on the acceleration function of the vehicle on the road segment to be driven.

[0064] Specifically, the process begins by interpolating the road segment to be traveled using a preset interpolation distance. This preset interpolation distance is a configurable setting that controls the fineness of the interpolation analysis. A smaller interpolation distance results in denser interpolation points, leading to a more refined predicted velocity function, but also increasing computational complexity. Conversely, a larger interpolation distance results in sparser interpolation points, improving computational efficiency but decreasing fitting accuracy. This preset interpolation distance can be configured based on actual computational resources and accuracy requirements. The starting point of the road segment to be traveled is used as the first interpolation point, with a distance of 0 between it and the starting point. Then, starting from the starting point, an interpolation point is set every preset interpolation distance along the travel direction of the road segment until the entire length of the road segment is covered. If the distance between the last interpolation point and the end point of the road segment is less than a preset interpolation distance, that end point is used as the last interpolation point. Through this interpolation segmentation process, the original road segment to be traveled as a single unit is decomposed into multiple interpolation points distributed at fixed intervals. These interpolation points are arranged sequentially along the road segment to be driven, forming a discrete position sequence, providing a spatial reference for subsequent construction of data points and function fitting. Each interpolation point has two core attributes: first, the distance between the interpolation point and the starting point of the road segment to be driven, which is calculated cumulatively along the driving direction of the road segment; second, the benchmark speed of the road segment corresponding to the interpolation point. Since the road conditions and road conditions are consistent within the same road segment to be driven, the benchmark speed of all interpolation points within the same road segment to be driven is the same, equal to the benchmark speed of the road segment to be driven.

[0065] After interpolation breaks are completed, multiple data points are constructed, with the distance between each interpolation point and the starting point of the road segment to be traveled as the x-axis and the base speed of the road segment corresponding to each interpolation point as the y-axis. Specifically, for the i-th interpolation point, its x-axis is x... i The ordinate of the interpolation point is the distance between the interpolation point and the starting point. i Let L be the reference speed of the road segment at the interpolation point. If the length of the road segment to be traveled is L and the preset interpolation distance is d, then approximately (L / d) + 1 interpolation points can be generated on the road segment to be traveled, corresponding to approximately (L / d) + 1 data points. Since the reference speed of all interpolation points within the same road segment to be traveled is the same, the ordinate values ​​of all data points within the road segment to be traveled are equal, and the data points are horizontally distributed in the velocity-position coordinate system. When the path to be traveled contains multiple consecutive road segments to be traveled, the ordinate values ​​of the data points in different road segments may change abruptly due to different reference speeds, forming a segmented constant discrete velocity distribution.

[0066] The least squares method is used to fit multiple data points to obtain the predicted speed function of the vehicle on the road segment to be traveled. The least squares method is an optimization fitting method that finds a continuous curve that minimizes the sum of squared deviations between the curve and each data point, thus achieving the optimal approximation of the overall trend of the discrete data points. The discrete road segment reference speed data provided by the navigation system, in units of road condition posts, is transformed into a continuously varying predicted speed function along the road segment to be traveled. This function, with distance as the independent variable and speed as the dependent variable, can describe the predicted driving speed of the vehicle at any position on the road segment to be traveled, providing a mathematical basis for subsequent calculation of the predicted acceleration at any position. The predicted speed function can be in polynomial form, such as a quadratic polynomial or a higher-order polynomial. The advantages of using a polynomial form are: the function is continuously differentiable, facilitating subsequent differentiation to obtain the acceleration function; the function form is simple, resulting in high computational efficiency; and the polynomial coefficients can be determined using the least squares method, allowing the fitted curve to optimally approximate each data point. During the fitting process, the calculation accuracy must be controlled within a preset error range to ensure that the predicted speed function accurately reflects the distribution trend of the data points. After obtaining the predicted speed function, boundary constraints need to be applied to ensure its physical reasonableness. Lower limit constraint: When the calculated result of the predicted speed function is negative, it is forcibly rewritten as 0, because vehicle speed cannot be negative. Upper limit constraint: When the calculated result of the predicted speed function exceeds a preset multiple of the road segment's baseline speed, it is forcibly rewritten as that multiple to prevent the fitted curve from generating unreasonably high speed values ​​due to polynomial extrapolation. After these constraints, the predicted speed function is physically reasonable across the entire road segment. Based on the predicted speed function, the distance values ​​of each interpolation point on the road segment are substituted into the function for calculation to obtain the predicted speed of the vehicle at each interpolation point. Since the predicted speed function is a continuous function, the predicted speed at any location on the road segment can be obtained, realizing the transformation from discrete road condition data to a continuous speed distribution.

[0067] Differentiating the predicted velocity function yields the vehicle's acceleration function on the road segment to be traveled. According to kinematic principles, acceleration is the rate of change of velocity with respect to time. Since the predicted velocity function uses distance as its independent variable, a chain rule is needed to differentiate it in conjunction with the velocity itself. Specifically, let the predicted velocity function be V(x), where x is the distance from the starting point. The acceleration function a(x) can be obtained as follows: First, differentiate V(x) with respect to x to obtain dV / dx. Then, combining V(x) itself, according to the definition of acceleration a = dV / dt = (dV / dx)×(dx / dt) = (dV / dx)×V(x), the acceleration function with distance as its independent variable is calculated. Substituting the distance values ​​of each interpolation point on the road segment to be traveled into the calculation using the acceleration function, the predicted acceleration of the vehicle at each interpolation point can be obtained. Similar to the predicted velocity, since the acceleration function is a continuous function, the predicted acceleration at any position on the road segment to be traveled can be obtained. In practical calculations, for ease of engineering implementation, spatial discretization can be used to calculate the predicted acceleration. On the predicted velocity function, two breakpoints are selected at fixed intervals (e.g., 10 meters), and the predicted velocities at the corresponding positions are extracted. The time difference between the two breakpoints is calculated, and then the average acceleration of that segment is calculated. The velocity at the previous point is recorded as the previous value of the predicted velocity, and the velocity at the next point is recorded as the subsequent value. The difference between the two values ​​divided by the time difference is the predicted acceleration for that segment.

[0068] Step S103: Based on the predicted speed and predicted acceleration of the vehicle in each road segment to be driven, calculate the predicted driving power of the vehicle in each road segment to be driven.

[0069] In this application, the coasting resistance of the vehicle in each planned road segment can first be calculated based on the predicted speed of the vehicle in each planned road segment. Then, the acceleration resistance of the vehicle in each planned road segment can be calculated based on the obtained total mass of the vehicle and the predicted acceleration of the vehicle in each planned road segment. Next, the slope resistance of the vehicle in each planned road segment can be calculated based on the obtained total mass of the vehicle and the obtained slope of each planned road segment. Finally, the predicted driving power of the vehicle in each planned road segment can be calculated based on the coasting resistance, acceleration resistance, slope resistance, and the predicted speed of the vehicle in each planned road segment.

[0070] Specifically, coasting resistance is the speed-related component of vehicle resistance during operation, composed of various factors such as air resistance, transmission system resistance, and road friction. There is a non-linear functional relationship between coasting resistance and predicted speed; specifically, air resistance is proportional to the square of the predicted speed, transmission system resistance is proportional to the linear term of the predicted speed, and constant terms such as road friction are independent of the predicted speed. Based on these physical characteristics, coasting resistance is calculated using the following quadratic function: Coasting Resistance = A × (Predicted Speed)2 +B×(predicted speed)+C. Where A is the drag coefficient related to the quadratic term of the predicted speed, reflecting the drag component related to the square of the speed, such as air resistance; B is the drag coefficient related to the linear term of the predicted speed, reflecting the drag component related to the linear term of the speed, such as transmission system resistance; C is a constant drag coefficient unrelated to the predicted speed, reflecting the constant drag component such as road friction. Coefficients A, B, and C are all vehicle constant parameters, determined according to the vehicle model calibration and do not change with driving conditions. Since the continuously changing predicted speed on the road segment to be driven has been obtained, the predicted speed values ​​at each interpolation point can be substituted into the above quadratic function to calculate the vehicle's coasting resistance at each position. The higher the predicted speed, the greater the coasting resistance, and the rate of increase exhibits a quadratic function-accelerated growth characteristic. When the vehicle is traveling on a high-speed section, the predicted speed is larger, and the quadratic component of the coasting resistance (air resistance) becomes the dominant factor; when the vehicle is traveling on a low-speed section, the predicted speed is smaller, and the relative proportion of the constant component (road friction) increases. Coasting resistance is always opposite to the vehicle's direction of travel and is the drag component that hinders the vehicle's forward movement. Regardless of whether the vehicle is accelerating, moving at a constant speed, or decelerating, the sliding resistance always exists, and its magnitude depends only on the instantaneous value of the predicted speed, regardless of the acceleration state.

[0071] Acceleration resistance is the inertial resistance component generated by a vehicle due to changes in speed. Its physical essence is the equivalent inertial force that the vehicle and its rotating components need to overcome during acceleration. When the predicted acceleration is positive (acceleration state), the acceleration resistance is opposite to the direction of travel, hindering vehicle acceleration; when the predicted acceleration is negative (deceleration state), the acceleration resistance is in the same direction as travel, manifesting as an inertial driving force. Acceleration resistance is calculated based on the vehicle's total mass and predicted acceleration: Acceleration resistance = δ × Total mass × Predicted acceleration. Wherein, total mass is the total mass of the vehicle involved in travel, including the vehicle's curb weight, passenger mass, and cargo mass. The vehicle's curb weight is the vehicle's base mass at the time of manufacture; passenger mass is determined based on the number of passengers, calculated according to a preset average mass per passenger, which can be determined by seat occupancy signals or door opening / closing status; cargo mass is the vehicle's load weight, which can be determined by configuration values ​​or preset constants. δ is the rotational mass coefficient, an additional coefficient used to equate the rotational inertia of rotating components such as wheels, drive shafts, and motor rotors to translational mass; this coefficient is a constant greater than 1. The magnitude and direction of acceleration drag directly depend on the predicted acceleration. The predicted acceleration function is obtained by differentiating the predicted velocity function; therefore, the predicted acceleration values ​​at each interpolation point can be substituted into the above formula to calculate the acceleration drag point by point. When the vehicle is in a state of rapid acceleration, the predicted acceleration is large, and the acceleration drag increases significantly, requiring the drive system to provide additional power to overcome this inertial drag. When the vehicle is traveling at a constant speed, the predicted acceleration is zero, and the acceleration drag disappears. When the vehicle is decelerating, the predicted acceleration is negative, and the acceleration drag is negative (i.e., inertial driving force), and this energy can be recovered or consumed.

[0072] Gradient resistance is the resistance or driving force formed by the component of gravity along the slope direction when a vehicle is traveling on a slope. When a vehicle is going uphill, the gradient resistance is opposite to the direction of travel, hindering the vehicle's progress; when a vehicle is going downhill, the gradient resistance is in the same direction as travel, manifesting as a gravitational driving force. Gradient resistance is calculated based on the vehicle's total mass and the slope of the road segment to be traveled: Gradient resistance = Total mass × g × sin(θ). Where g is the constant gravitational acceleration; θ is the angle value of the road segment slope, which is provided by the navigation system and can be expressed as slope angle or length-to-height ratio. If the navigation system does not provide road segment slope information for a certain road segment to be traveled, the road segment slope is calculated as 0 degrees, that is, the gradient resistance is 0, indicating that the road segment is a level surface. Unlike coasting resistance and acceleration resistance, which change continuously at each interpolation point, gradient resistance is a constant value within the same road segment to be traveled. Because the road slope is a road segment attribute at the plinth level, the slope is the same at all locations within the same road segment to be driven. Therefore, for the same road segment to be driven, the total mass is constant, the road slope is constant, and the slope resistance is constant throughout the entire range of that road segment.

[0073] After obtaining the sliding resistance, acceleration resistance, and gradient resistance, the three resistances are algebraically summed to obtain the total driving resistance of the vehicle on each road segment: Total Driving Resistance = Sliding Resistance + Acceleration Resistance + Gradient Resistance. This summation is an algebraic operation and the directional characteristics of each resistance must be considered. When the vehicle decelerates downhill, the acceleration resistance (inertial driving force) and gradient resistance (gravitational driving force) may be in the same direction as the vehicle's movement, exhibiting negative resistance values. In this case, the total driving resistance may decrease or even become negative, indicating that the vehicle can coast by inertia or gravity without needing to be driven. Predicted driving power is the mechanical power at the wheel ends required for the vehicle to overcome the total driving resistance and maintain the predicted speed. Power equals the product of force and velocity; therefore, predicted driving power = total driving resistance × predicted speed. Substituting the expression for total driving resistance, we get predicted driving power = (sliding resistance + acceleration resistance + gradient resistance) × predicted speed. Since coasting resistance, acceleration resistance, gradient resistance, and predicted speed are all functions of the position along the road segment to be traveled, the predicted driving power is also a continuous function of the position along the road segment to be traveled.

[0074] By substituting the sliding resistance, acceleration resistance, gradient resistance, and predicted speed values ​​at each interpolation point into the above formula, the predicted drive power can be calculated point by point. The physical meaning of predicted drive power is: assuming the vehicle is traveling according to the predicted driving state (predicted speed, predicted acceleration), the mechanical power the drive system needs to output at the wheel ends to overcome all the driving resistance under this state. When the predicted drive power is positive, it indicates that the drive system needs to output positive power to propel the vehicle forward; when the predicted drive power is negative, it indicates that the vehicle's driving resistance is negative (there is a driving force component). If the vehicle is in an electric drive state, this negative power can be partially converted into electrical energy storage through braking energy recovery; if the deceleration is too large, the hydraulic braking system will intervene, and the excess energy will be dissipated as heat. Predicted drive power is a key intermediate quantity connecting the vehicle's motion state and energy consumption. The predicted driving state (predicted speed, predicted acceleration) reflects the vehicle's kinematic characteristics, while the calculated predicted drive power reflects the mechanical power required to maintain this motion state. The predicted drive power does not yet take into account the energy conversion efficiency of the electric drive system, transmission system, and battery system. It is the mechanical power demand at the wheel end. The energy conversion needs to be combined with the efficiency coefficients of each system to obtain the electrical energy consumption at the battery end.

[0075] Further, after calculating the predicted driving power of the vehicle in each planned road segment based on the predicted speed and predicted acceleration of the vehicle in each planned road segment, the method may further include the following steps: Step S201: Determine the regenerative braking energy of the vehicle in each planned road segment based on the predicted speed and predicted acceleration of the vehicle in each planned road segment. Step S202: Correct the predicted driving power of the vehicle in each planned road segment based on the regenerative braking energy of the vehicle in each planned road segment to obtain the first corrected driving power of the vehicle in each planned road segment. Step S203: Update the predicted driving power of the vehicle in each planned road segment to the first corrected driving power of the vehicle in each planned road segment. For details, please refer to... Figure 2 , Figure 2 This is a schematic diagram illustrating a process for updating the predicted driving power of a vehicle on each road segment to be driven, as provided in an embodiment of this application.

[0076] Specifically, regenerative braking energy is the recoverable energy generated by the drive motor acting as a generator during vehicle deceleration, converting the vehicle's kinetic energy into electrical energy and feeding it back to the battery system. When the predicted acceleration is negative (i.e., the vehicle is decelerating), the vehicle has the potential for regenerative braking energy. Determining regenerative braking energy requires a comprehensive assessment of the vehicle's current deceleration state and the boundary conditions for recoverable energy, based on the obtained predicted speed and predicted acceleration. First, it is determined whether the predicted acceleration meets the activation conditions for regenerative braking. When the predicted acceleration is negative, it indicates that the vehicle is decelerating and there is a possibility of regenerative braking energy. When the predicted acceleration is positive or zero, the vehicle is accelerating or moving at a constant speed, and there is no regenerative braking energy; therefore, the regenerative braking energy is determined to be zero. Second, assuming the predicted acceleration is negative, it is further determined whether the predicted speed meets the speed threshold condition for regenerative braking. When the predicted speed is below the preset speed threshold, the regenerative braking system does not operate due to low motor power generation efficiency or system protection requirements, and the regenerative braking energy is determined to be zero. When the predicted speed reaches or exceeds the preset speed threshold, the regenerative braking system is ready to operate, and the recoverable energy needs to be further calculated. Secondly, under the condition of simultaneously satisfying negative predicted acceleration and high predicted speed, the intervention depth of regenerative braking is determined based on the absolute value of the predicted acceleration. When the absolute value of the predicted acceleration is within a preset range, the drive motor can independently complete the braking torque output to achieve energy recovery; when the absolute value of the predicted acceleration exceeds the preset upper limit, the hydraulic braking system will intervene to supplement the braking torque, and the excess braking energy exceeding the motor's recovery capacity will no longer be recovered and will be dissipated as heat. Under the premise of satisfying all the above activation conditions, the value of regenerative braking energy is determined by the product of the negative resistance component in the total driving resistance and the predicted speed. Specifically, when the predicted acceleration is negative, the acceleration resistance is negative (manifested as inertial driving force). If the total driving resistance is negative overall or a portion of it can drive the vehicle to decelerate, then this portion of negative power is the theoretical energy source for regenerative braking. At the same time, the actual value of regenerative braking energy is constrained by the maximum charging power of the battery system, that is, the recovered energy must not exceed the current maximum allowable charging power of the battery pack, and any excess cannot be recovered.

[0077] After obtaining the regenerative braking energy, the calculated predicted drive power needs to be corrected to obtain the first corrected drive power. The calculated predicted drive power is the total mechanical power demand at the wheel end, without considering the offsetting effect of regenerative energy during deceleration. In actual vehicle operation, when the regenerative braking system is working, the drive system does not need to output all the predicted drive power; some deceleration energy can be recovered by the motor. Therefore, the regenerative portion needs to be subtracted from the predicted drive power to obtain the net power that the drive system actually needs to output. The first corrected drive power is calculated as follows: First corrected drive power = Predicted drive power - Regenerative braking energy. The above correction is an algebraic operation. When the vehicle is accelerating or moving at a constant speed, the regenerative braking energy is zero, the first corrected drive power equals the predicted drive power, and the correction has no practical effect. When the vehicle is in a state of slight deceleration, the regenerative braking energy is positive, the first corrected drive power is less than the predicted drive power, indicating that the power that the drive system actually needs to output is reduced due to energy recovery. When a vehicle is undergoing severe deceleration, if the regenerative braking energy reaches its saturation limit (limited by maximum charging power or hydraulic braking intervention), the excess cannot be offset. The first corrected drive power is the remaining power requirement after deducting the saturated regenerative energy. The first corrected drive power must meet physical constraints: when the correction result is still positive, it means the drive system needs to output positive power to maintain driving; when the correction result is zero, it means the vehicle relies on inertia to coast, and the drive system does not need to output power; when the correction result is negative and the regenerative braking system is working, this negative power represents the recoverable feedback power, which will be absorbed by the battery system in subsequent energy conversion stages.

[0078] The predicted drive power for each road segment to be traveled is updated to the first corrected drive power. This update means that in subsequent energy consumption prediction calculations per unit mile, the net drive power after deducting regenerative braking energy will be used as the basis for energy consumption calculations, rather than the total drive power that does not consider the regeneration effect. For the same road segment to be traveled, the value of regenerative braking energy may differ at different locations due to variations in predicted speed and predicted acceleration along the segment's position. Therefore, the first corrected drive power is also a function of variations along the segment's position. At locations with dense interpolation points, regenerative braking energy needs to be calculated point-by-point and the predicted drive power corrected point-by-point; at locations where the predicted acceleration is positive or the predicted speed is below a threshold, the regenerative braking energy is zero, and the first corrected drive power equals the original predicted drive power, requiring no actual correction.

[0079] Furthermore, after calculating the predicted driving power of the vehicle in each road segment based on the predicted speed and predicted acceleration of the vehicle in each road segment to be driven, the method may further include the following steps: Step S301: Calculate the driving power correction coefficient of the vehicle based on the acquired historical predicted driving power and historical actual driving power of the vehicle in each road segment to be driven. S302: Correct the predicted driving power of the vehicle in each road segment to be driven based on the driving power correction coefficient of the vehicle, to obtain the second corrected driving power of the vehicle in each road segment to be driven. S303: Update the predicted driving power of the vehicle in each road segment to be driven with the second corrected driving power of the vehicle in each road segment to be driven. See details for further reference. Figure 3 , Figure 3 This is a schematic diagram illustrating a process for updating the predicted driving power of a vehicle on each road segment to be driven, as provided in an embodiment of this application.

[0080] Specifically, historical predicted driving power refers to the predicted driving power of a road segment calculated based on navigation data such as the segment length, reference speed, and gradient, according to steps S102 and S103, when the vehicle previously traveled through a road segment that is the same as or similar to the current road segment. This historical predicted driving power has been calculated and recorded in the vehicle's historical journey and serves as the benchmark for the current prediction value. Historical actual driving power refers to the actual driving power calculated from real-time vehicle operation data collected by onboard sensors when the vehicle actually traveled through the corresponding road segment. This historical actual driving power reflects the actual energy consumption level of the vehicle under real-time driving conditions on that road segment and serves as a true benchmark for verifying the accuracy of the prediction. The acquisition of the above historical data is based on a single journey. After the vehicle completes a journey, the predicted driving power and actual driving power of each road segment in that journey are associated and stored to form a historical data record that can be retrieved later.

[0081] For a specific road segment previously traveled by the vehicle, the historical predicted driving power and historical actual driving power of that segment are extracted, and the ratio between the two is calculated: Driving power correction coefficient = Historical actual driving power / Historical predicted driving power. The physical meaning of this ratio is the deviation multiple of the historical actual driving power relative to the historical predicted driving power. When this coefficient is greater than 1, it indicates that the historical prediction underestimated the actual driving power demand; when the coefficient is less than 1, it indicates that the historical prediction overestimated the actual driving power demand; when the coefficient equals 1, it indicates that the historical prediction perfectly matches the actual demand. If the vehicle has multiple historical driving records on the same road segment, the driving power correction coefficients calculated multiple times need to be comprehensively processed to obtain a correction coefficient representing the long-term statistical characteristics of that road segment. The comprehensive processing method can be to use an arithmetic average of the historical correction coefficients from the most recent preset number of times, so that the correction coefficient reflects both the latest operating conditions and avoids excessive fluctuations caused by single abnormal data. If no completely matching historical road segment record is found on the current driving route, the driving power correction coefficient of historical road segments close to the current driving route can be extracted. The determination of similar road segments is based on the similarity of road segment attributes, including the degree of matching of features such as road type, road condition, and road slope. The closest historical record is selected within a preset tolerance range. If the vehicle has neither a completely matching historical road segment record nor a similar road segment record that meets the similarity requirements on the current route to be traveled, then the route to be traveled is a new route for the vehicle. For new routes, due to the lack of historical benchmark data, the drive power correction coefficient is initialized to 1, meaning that the predicted drive power is not corrected for the time being. After the current trip is completed, the predicted drive power of the current trip is compared with the actual drive power to calculate and generate the initial drive power correction coefficient for the route, which will be used in subsequent trips.

[0082] After obtaining the drive power correction coefficient, the calculated predicted drive power needs to be corrected to obtain the second corrected drive power. Although the predicted drive power calculated based on the physical model and navigation data considers multidimensional driving resistance and regenerative braking effects, it may still exhibit systematic deviations from the actual drive power due to model simplification, parameter bias, and unmodeled factors (such as differences in driver driving style, micro-fluctuations in real-time traffic flow, and the impact of ambient temperature on battery and transmission efficiency). The drive power correction coefficient encapsulates the empirical patterns of prediction deviations from historical journeys. By applying this coefficient to the current prediction, the aforementioned systematic deviations can be compensated, making the corrected drive power closer to reality. The second corrected drive power is calculated as follows: Second corrected drive power = Predicted drive power × Drive power correction coefficient. The above correction involves a multiplication operation. When the drive power correction coefficient is greater than 1, the second corrected drive power is greater than the predicted drive power, indicating that historical experience shows the actual energy consumption of this road segment is higher than the model prediction, and the predicted value needs to be adjusted upwards. When the drive power correction coefficient is less than 1, the second corrected drive power is less than the predicted drive power, indicating that historical experience shows the actual energy consumption of this road segment is lower than the model prediction, and the predicted value needs to be adjusted downwards. When the drive power correction coefficient is equal to 1, the second corrected drive power is equal to the predicted drive power, and the correction has no practical effect. The drive power correction coefficient can be applied uniformly to the entire driving path or applied separately to each driving segment. When using segment-level correction, for each driving segment in the current driving path, the historical drive power correction coefficient of that segment or nearby segments is found, and the second corrected drive power of each segment is calculated separately, achieving refined segment-level deviation compensation. When using path-level unified correction, the same drive power correction coefficient is applied to the entire driving path, and this coefficient can be a comprehensive representative value of the historical correction coefficients of each segment in the path.

[0083] The predicted drive power for each road segment is updated to a second corrected drive power. This update means that in subsequent energy consumption prediction calculations per unit mile, the net drive power corrected by historical experience will be used as the basis for energy consumption calculations, rather than the original predicted drive power calculated solely based on the physical model. The calculation and application of the drive power correction coefficient has iterative evolutionary characteristics. After each trip, the actual drive power for each road segment in this trip is compared again with the updated predicted drive power (i.e., the second corrected drive power), a new drive power correction coefficient is calculated, and it is merged and updated with the historical correction coefficients. As the number of times the vehicle travels on the same or similar paths increases, the drive power correction coefficient is continuously calibrated by new actual data, gradually converging to a stable value that reflects the true energy consumption characteristics of that path, thus continuously improving the accuracy of subsequent predictions.

[0084] Step S104: Based on the predicted driving power of the vehicle in each road segment to be driven and the length of each road segment to be driven, predict the energy consumption per unit mile of the vehicle on the road to be driven.

[0085] In this application, the predicted driving power of the vehicle on each road segment to be driven is first integrated along the length of each road segment to obtain the total mechanical energy of the vehicle on the road to be driven. Then, the total mechanical energy of the vehicle on the road to be driven is converted to obtain the total battery energy consumption of the vehicle on the road to be driven. Finally, based on the length of each road segment to be driven, the total length of the road to be driven is calculated, and based on the total battery energy consumption of the vehicle on the road to be driven and the total length of the road to be driven, the energy consumption per unit mile of the vehicle on the road to be driven is predicted.

[0086] Specifically, the predicted driving power of the vehicle along each road segment to be traveled is integrated along the length of that segment to obtain the total mechanical energy of the vehicle on the path to be traveled. The physical meaning of this integration is that the predicted driving power is the mechanical work that the drive system needs to output per unit time, and mechanical energy is equal to the power integral over time. Since the vehicle travels at the predicted speed, and the road segment length is equal to the speed integral over time, integrating the predicted driving power along the road segment length is equivalent to integrating the power over time, yielding the total mechanical work that the drive system needs to output on the path to be traveled. The integral calculation of the total mechanical energy can be expressed as: Total Mechanical Energy = ∫Predicted Driving Power dx. Here, the integration variable x is the cumulative distance along the path to be traveled, and the integration interval is from the start to the end of the path. Since the predicted driving power is a continuous function of the position change along the road segment to be traveled, the above integration can be achieved through numerical integration methods. In practical implementation, the path to be traveled is divided into multiple small distance segments. Within each segment, a representative value of the predicted driving power (such as the predicted driving power at the start of the segment, the predicted driving power at the end of the segment, or the average of the predicted driving power at both ends of the segment) is taken and multiplied by the distance length of that segment to obtain the mechanical energy element of that segment. The mechanical energy elements of all segments are summed to obtain an approximate total mechanical energy. The finer the segment division, the closer the numerical integration result is to the true integral value. The path to be traveled consists of multiple segments connected sequentially. The calculation of the total mechanical energy requires traversing all segments. For each segment, the predicted driving power within that segment is integrated along its length to obtain the mechanical energy of that segment. Then, the mechanical energies of all segments are summed to obtain the total mechanical energy of the path to be traveled. This method of segmented integration and then splicing matches the segmented data structure of the road condition column in the navigation system, facilitating engineering implementation.

[0087] Total mechanical energy is the mechanical work that the drive system needs to output at the wheels, but the vehicle actually consumes electrical energy provided by the battery system. The conversion from mechanical energy to electrical energy must consider various efficiency losses during energy transfer, including the electrical-to-mechanical energy conversion efficiency of the electric drive system, the mechanical transmission efficiency of the transmission system, and the charging and discharging efficiency of the battery system. To convert the total mechanical energy and obtain the total battery energy consumption of the vehicle on the desired travel path, the following efficiency chain model should be applied: Total battery energy consumption = Total mechanical energy / (Electric drive efficiency × Transmission efficiency × Battery charging and discharging efficiency). Here, the electric drive efficiency is the efficiency of the drive motor and power electronics in converting battery electrical energy into wheel mechanical energy. This efficiency is related to the motor's operating point (such as speed and torque) and remains at a high level near the rated operating range. The transmission efficiency is the efficiency of the reducer, differential, and other transmission components in transmitting the mechanical energy from the motor output shaft to the wheels, and is a constant close to 1. The battery charging and discharging efficiency is the energy conversion efficiency of the battery during charging and discharging, taking into account losses such as heat generation due to battery internal resistance. The product of these three efficiencies constitutes the total energy conversion efficiency from battery electrical energy to wheel mechanical energy. Dividing the total mechanical energy by the total efficiency yields the battery energy consumption required to output that mechanical energy, i.e., the total battery energy consumption. Electric drive efficiency, transmission efficiency, and battery charge / discharge efficiency are all vehicle constant parameters, determined according to the vehicle model calibration. Transmission efficiency is a fixed constant; while electric drive efficiency and battery charge / discharge efficiency fluctuate slightly with operating conditions, calibration values ​​representing typical levels for the vehicle model are used in energy consumption prediction to ensure the determinism and repeatability of the calculations.

[0088] Based on the length of each road segment to be traveled, the total length of the route to be traveled is calculated. The lengths of all road segments included in the route to be traveled are then summed arithmetically to obtain the cumulative distance from the start to the end of the route. This total length is the total mileage required for the vehicle to complete the route. Based on the total battery energy consumption and the total length of the route, the vehicle's energy consumption per unit mile on the route is predicted: Energy consumption per unit mile = Total battery energy consumption / Total length of the route. The physical meaning of energy consumption per unit mile is the amount of battery energy consumed by the vehicle to complete a unit of travel distance (e.g., per 100 kilometers), expressed in kilowatt-hours per 100 kilometers. This indicator normalizes the total energy consumption of the route to a mileage benchmark, facilitating comparisons of energy consumption levels across different route lengths and providing users with an intuitive understanding of the vehicle's energy efficiency performance.

[0089] Furthermore, after predicting the vehicle's energy consumption per unit mile on the planned driving path based on the predicted driving power and the length of each planned driving path, the method may further include the following steps: First, the vehicle's remaining battery energy and energy consumption per unit mile on the planned driving path can be used to predict the vehicle's driving range. Then, the vehicle's driving range can be displayed based on the acquired vehicle operating conditions. Further still, when the vehicle's operating conditions change, the absolute value of the difference between the currently displayed driving range and the previously displayed driving range is calculated; it is determined whether the absolute value of the difference is greater than a preset value. If the absolute value of the difference is greater than the preset value, a prompt message is generated and displayed. The prompt message is used to indicate the change in the vehicle's driving range.

[0090] Specifically, remaining battery energy refers to the total electrical energy that the vehicle's power battery can currently output, which is monitored and output in real time by the battery management system. This remaining battery energy differs from the battery's state of charge (SOC). SOC is expressed as a percentage, representing the current battery capacity relative to its rated capacity, while remaining battery energy is expressed in units of energy (such as kilowatt-hours) as the absolute amount of electrical energy that the battery can currently use. Remaining battery energy comprehensively considers the impact of factors such as the battery's current SOC, battery temperature, and battery aging status on the actual usable energy, and is the direct energy basis for calculating the driving range. Based on the remaining battery energy and energy consumption per unit mile, the vehicle's driving range is predicted as follows: Driving range = Remaining battery energy / Energy consumption per unit mile. The physical meaning of this calculation is the remaining driving range that can be supported by the vehicle's currently available remaining battery energy, according to the predicted energy consumption level per unit mile on the route to be traveled. When the route to be traveled is a specific route planned by navigation, the driving range represents the remaining distance that the vehicle can travel along that route; when navigation is not enabled or a general range estimate is required, the driving range can be estimated based on the default route or average energy consumption level. The driving range is a dynamic prediction that is updated in real time as the vehicle's status changes. When the remaining battery energy changes due to driving or charging, the driving range will increase or decrease accordingly. When the navigation route changes, resulting in a change in the route to be traveled, the energy consumption per unit distance needs to be recalculated and the driving range updated. When changes in vehicle operating conditions (such as turning on the air conditioner or entering a different temperature environment) affect the actual energy consumption, the driving range also needs to be adjusted accordingly.

[0091] Vehicle operating conditions refer to the vehicle's current operating status and environmental conditions, including but not limited to: vehicle speed, acceleration, air conditioning on / off status, air conditioning power, ambient temperature, battery temperature, and driving mode (e.g., Eco / Standard / Sport). This operating condition information is collected in real-time by onboard sensors and controllers to determine whether the vehicle is currently in a steady-state driving state or experiencing significant changes in operating conditions. Based on vehicle operating conditions, the displayed remaining range must adhere to a display verification mechanism to avoid frequent fluctuations in the remaining range value that could affect user trust. Specifically, when the vehicle operating conditions remain unchanged, the newly calculated remaining range is compared with the currently displayed value. If the new value is less than the currently displayed value and the difference is within a preset threshold, the new value is updated; if the difference exceeds the preset threshold, the current displayed value is maintained and counted. Only when multiple consecutive calculations exceed this threshold is the display updated. This verification mechanism prevents display flickering caused by computational noise or minor fluctuations in operating conditions. When vehicle operating conditions change, the newly calculated remaining range is compared with the result of the previous cycle in the next calculation cycle. If the difference reaches or exceeds the preset jump threshold, it is determined to be a significant change in range caused by a sudden change in operating conditions. At this time, a secondary display explanation signal is sent, which is simultaneously sent to the instrument panel and the vehicle's main unit for display, providing the user with an explanation of the reason for the change in mileage. The display of driving range supports multiple modes, which users can switch between via the instrument panel, the vehicle's main unit, or a mobile terminal application. Display modes include, but are not limited to: standard driving range based on standard operating conditions (such as NEDC, WLTP), dynamic driving range based on historical average energy consumption, and accurate dynamic driving range calculated by a specific method. When the user selects the display mode based on navigation route energy consumption prediction, the complete calculation process is activated; when the user selects other display modes, the driving range is calculated and displayed according to the rules of the corresponding mode. When navigation data or cloud calculation results cannot be obtained, the display system activates a degradation strategy, that is, it uses the average energy consumption calculated locally on the vehicle instead of the energy consumption per unit mile based on route prediction to calculate and display the driving range. This degradation strategy ensures the continuous availability of the driving range display, providing users with driving range reference information even when communication is interrupted or navigation is not enabled.

[0092] In summary, this application provides a vehicle energy consumption prediction method. This method obtains the length of each road segment and the reference speed of that segment along the vehicle's planned travel path. For each road segment, based on the segment length and reference speed, the predicted speed and acceleration of the vehicle in that segment are determined. Based on the predicted speed and acceleration of the vehicle in each road segment, the predicted driving power of the vehicle in each road segment is calculated. Based on the predicted driving power and the length of each road segment, the energy consumption per unit mile of the vehicle along the planned travel path is predicted. This method can more accurately reflect the energy consumption level of the vehicle on the actual planned travel path, improving the accuracy of the predicted energy consumption per unit mile.

[0093] This application also provides an electronic device in its embodiments. (See reference...) Figure 4 The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0094] like Figure 4 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 408 into a random access memory (RAM) 403. When the electronic device is powered on, the RAM 403 also stores various programs and data required for the operation of the electronic device. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0095] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 408 including, for example, memory cards, hard drives, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0096] This application also provides a computer program product including computer-readable instructions, which, when executed on an electronic device, cause the electronic device to implement any of the vehicle energy consumption prediction methods provided in this application.

[0097] This application also provides a computer-readable storage medium carrying one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the vehicle energy consumption prediction methods provided in this application.

[0098] This application also provides a vehicle, including the electronic equipment provided in this application embodiment.

[0099] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the accompanying drawings of the device embodiments provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods of the various embodiments of this application.

[0101] In the above embodiments, the implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, in the form of a computer program product.

[0102] 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 this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may 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)).

Claims

1. A method for predicting vehicle energy consumption, characterized in that, include: Obtain the length of each road segment and the base speed of each segment in the vehicle's planned route. For each road segment to be driven, based on the road segment length and the road segment reference speed, the predicted speed and predicted acceleration of the vehicle in the road segment to be driven are determined; Based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be driven, the predicted driving power of the vehicle in each of the road segments to be driven is calculated. Based on the predicted driving power of the vehicle in each of the road segments to be driven and the length of each road segment to be driven, the energy consumption per unit mile of the vehicle on the road to be driven is predicted.

2. The vehicle energy consumption prediction method according to claim 1, characterized in that, The step of calculating the predicted driving power of the vehicle in each of the predicted road segments based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be driven includes: Based on the predicted speed of the vehicle in each of the road segments to be traveled, the sliding resistance of the vehicle in each of the road segments to be traveled is calculated. Based on the obtained total mass of the vehicle and the predicted acceleration of the vehicle in each of the road segments to be driven, the acceleration resistance of the vehicle in each of the road segments to be driven is calculated. Based on the total mass of the vehicle and the slope of each road segment to be driven, the slope resistance of the vehicle on each road segment to be driven is calculated. Based on the vehicle's coasting resistance, acceleration resistance, and gradient resistance in each of the road segments to be driven, as well as the vehicle's predicted speed in each of the road segments to be driven, the predicted driving power of the vehicle in each of the road segments to be driven is calculated.

3. The vehicle energy consumption prediction method according to claim 1 or 2, characterized in that, After calculating the predicted driving power of the vehicle in each of the predicted road segments based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be traveled, the method further includes: Based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be driven, the braking energy recovery of the vehicle in each of the road segments to be driven is determined. Based on the regenerative braking energy of the vehicle in each of the road segments to be driven, the predicted driving power of the vehicle in each of the road segments to be driven is corrected to obtain the first corrected driving power of the vehicle in each of the road segments to be driven. The predicted driving power of the vehicle in each of the road segments to be driven is updated to the first corrected driving power of the vehicle in each of the road segments to be driven.

4. The vehicle energy consumption prediction method according to claim 1 or 2, characterized in that, After calculating the predicted driving power of the vehicle in each of the predicted road segments based on the predicted speed and predicted acceleration of the vehicle in each of the road segments to be traveled, the method further includes: Based on the historical predicted driving power and historical actual driving power of the vehicle in each of the road segments to be driven, the driving power correction coefficient of the vehicle is calculated. Based on the vehicle's drive power correction coefficient, the predicted drive power of the vehicle in each of the road segments to be driven is corrected to obtain the second corrected drive power of the vehicle in each of the road segments to be driven. The predicted driving power of the vehicle in each of the road segments to be driven is updated to the second corrected driving power of the vehicle in each of the road segments to be driven.

5. The vehicle energy consumption prediction method according to claim 1, characterized in that, For each road segment to be traveled, based on the segment length and the reference vehicle speed, the method for determining the predicted speed and predicted acceleration of the vehicle in that road segment includes: Based on the preset interpolation distance and the length of the road segment to be driven, the road segment to be driven is interpolated and broken to obtain multiple interpolation points; Multiple data points are constructed using the distance between each interpolation point and the starting point of the road segment to be driven as the abscissa and the road segment reference speed as the ordinate. By fitting the multiple data points, a predicted speed function of the vehicle on the road segment to be driven is obtained, and based on the predicted speed function of the vehicle on the road segment to be driven, the predicted speed of the vehicle on the road segment to be driven is obtained. Differentiate the predicted speed function of the vehicle on the road segment to be driven to obtain the acceleration function of the vehicle on the road segment to be driven, and obtain the predicted acceleration of the vehicle on the road segment to be driven based on the acceleration function of the vehicle on the road segment to be driven.

6. The vehicle energy consumption prediction method according to claim 1, characterized in that, The prediction of the vehicle's energy consumption per unit mile on the planned travel path, based on the predicted driving power of the vehicle in each of the planned travel segments and the length of each planned travel segment, includes: The predicted driving power of the vehicle in each of the road segments to be driven is integrated along the length of each road segment to obtain the total mechanical energy of the vehicle on the road to be driven. The total mechanical energy of the vehicle on the driving path is converted to obtain the total battery energy consumption of the vehicle on the driving path. Based on the length of each road segment to be driven, the total length of the route to be driven is calculated, and based on the total battery energy consumption of the vehicle on the route to be driven and the total length of the route to be driven, the energy consumption per unit mile of the vehicle on the route to be driven is predicted.

7. The vehicle energy consumption prediction method according to claim 1 or 6, characterized in that, After predicting the energy consumption per unit mile of the vehicle on the planned travel path based on the predicted driving power of the vehicle in each of the planned travel segments and the length of each planned travel segment, the method further includes: Based on the remaining battery energy of the vehicle and the energy consumption per unit mile of the vehicle on the route to be traveled, the driving range of the vehicle is predicted. Based on the obtained vehicle operating conditions, the vehicle's remaining driving range is displayed.

8. The vehicle energy consumption prediction method according to claim 1, characterized in that, After displaying the vehicle's remaining driving range based on the acquired vehicle operating conditions, the method further includes: When the vehicle's operating condition changes, calculate the absolute value of the difference between the currently displayed driving range and the previously displayed driving range. Determine whether the absolute value of the difference is greater than a preset value. If the absolute value of the difference is greater than the preset value, generate a prompt message and display the prompt message, which is used to indicate the change in the vehicle's remaining driving range.

9. An electronic device, characterized in that, It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program to enable the electronic device to implement the vehicle energy consumption prediction method as described in any one of claims 1 to 8.

10. A vehicle, characterized in that, Includes the electronic device as described in claim 9.

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