Commercial vehicle endurance and income prediction method

Abstract

The invention relates to the technical field of commercial vehicle big data application, and relates to a commercial vehicle endurance and income prediction method. And in response to a prediction request of a driver, obtaining a destination, load information, freight information, vehicle state data and driving behavior data of a freight travel input by the driver. And determining a travel route and road information according to the destination. And determining a road working condition weight, a driving behavior weight and a load weight according to the load information, the road information and the driving behavior data. And determining energy consumption data of the commercial vehicle according to the vehicle type and each weight of the commercial vehicle. And according to the energy consumption data and the vehicle state data, judging whether the endurance of the commercial vehicle meets the freight travel or not. If yes, according to the energy consumption data, the freight information and the road information, the income of the freight travel is determined, and a travel estimation page is displayed through a display device on the commercial vehicle. A driver can predict whether the income of the travel and the vehicle endurance are enough or not in advance before the freight travel is started.

Description

Technical Field

[0001] This manual relates to the field of big data application technology for commercial vehicles, and in particular to a method for predicting the range and revenue of commercial vehicles. Background Technology

[0002] Driven by the rapid development of new energy technologies and the increasing global awareness of environmental protection, new energy commercial vehicles have become an important part of the logistics and transportation sector. At this crucial stage of the accelerated penetration of new energy commercial vehicles into the logistics and transportation field, accurate range and revenue forecasting have become core requirements for improving operational efficiency and ensuring driver income.

[0003] Current market solutions, such as those described in "A Method and System for Energy Distribution in New Energy Light Trucks" or "A Method, Device, and Storage Medium for Generating Driving Suggestions for New Energy Commercial Vehicles," primarily rely on single-dimensional data (e.g., simple vehicle load or basic road condition information) for range estimation and revenue analysis. However, these solutions fail to systematically incorporate road condition parameters and other parameters clearly defined in national or industry standards, resulting in range prediction deviations generally exceeding 15%, severely hindering route planning and power management in actual transportation.

[0004] Specifically, existing technologies have two significant shortcomings: First, regarding road condition modeling, although Article 5.3 of GB / T 18386-2021 "Test Methods for Energy Consumption Rate and Driving Range of Electric Vehicles" clearly stipulates that slope tests should cover a range of -10° to 10° and requires the inclusion of slope resistance in energy consumption calculations, current solutions only use rough classification methods such as "steep slope / flat road," failing to accurately quantify energy consumption, resulting in significant errors in energy consumption correction. Second, regarding the impact of different road surface types, Appendix C of GB / T 36672-2018 "Technical Requirements for Remote Service and Management System of Electric Vehicles" clearly classifies road surfaces into four categories: asphalt, cement, dirt, and ice / snow, and provides corresponding energy consumption coefficients. However, existing systems fail to strictly follow this standard in modeling road surface types, resulting in insufficient ability to correct energy consumption based on road conditions, further expanding the error range of driving range prediction.

[0005] Furthermore, because the key order information is not deeply integrated with the aforementioned national standard energy consumption parameters, the existing system is also unable to provide a profit forecast that fits the actual scenario, making it difficult to meet the urgent need of freight drivers to make an integrated and accurate prediction of "range feasibility" and "economic benefits" before accepting an order.

[0006] Therefore, this specification provides a method for predicting the range and revenue of commercial vehicles. Summary of the Invention

[0007] This specification provides a method for predicting the range and revenue of commercial vehicles to address the aforementioned problems in the prior art.

[0008] The following technical solution is adopted in this specification: This manual provides a method for predicting the range and revenue of commercial vehicles, including: S1. In response to the prediction request from the driver in the commercial vehicle, obtain the destination, load information, freight information, vehicle status data and driving behavior data of the freight trip input by the driver; S2. Based on the destination, determine the travel route and the road information corresponding to the travel route from the map data unit; S3. Determine the load weight, road condition weight, and driving behavior weight based on the load information, road information, and driving behavior data; S4. Determine the energy consumption data of the commercial vehicle based on the vehicle type, road condition weight, driving behavior weight, and load weight; S5. Based on the energy consumption data and the vehicle status data, determine whether the commercial vehicle's range meets the freight journey requirements; S6. If so, the revenue of the freight trip is determined based on the energy consumption data, the freight cost information and the road information, and a trip estimation page is displayed on the display device on the commercial vehicle. The content displayed on the estimation page includes at least the revenue, the energy consumption data and text information indicating that the freight trip can reach the destination. S7. If not, then display a prompt message to the driver through the display device, the prompt message being used to remind the driver that the commercial vehicle's range does not meet the freight trip requirements.

[0009] Based on the aforementioned technical means, this solution accurately calculates the required range for freight trips, taking into account factors such as load capacity, road conditions, and driving style. Furthermore, it allows drivers to anticipate the profitability of their trip and whether the vehicle's range is sufficient before departure, improving driver efficiency. The onboard display visually presents information such as "delivery capability," "estimated revenue," and "energy consumption details," reducing driver cognitive burden and improving system usability. This achieves high-precision range assessment and economic benefit prediction for single freight missions of commercial vehicles, combining technological advancement with commercial practicality.

[0010] Furthermore, the road information includes the gradient of the route, road surface type, road weather data, and road traffic density; S3 determines the road condition weights based on the road information, specifically including: Based on the slope of the route, determine the slope energy efficiency coefficient; based on the road surface type, determine the road surface energy consumption coefficient; based on the road weather data, determine the rainfall energy consumption coefficient; and based on the road traffic density, determine the traffic density energy consumption coefficient. The road condition weights are determined based on the slope energy efficiency coefficient, the road surface energy consumption coefficient, the rainfall energy consumption coefficient, and the traffic density energy consumption coefficient.

[0011] Based on the aforementioned technical means, it is possible to follow the requirements of Article 5.3 of "GB / T 18386-2021 Test Method for Energy Consumption Rate and Driving Range of Electric Vehicles," which clearly stipulates that the slope test should cover the range of -10° to 10°, and the four road surface types (asphalt, cement, dirt road, and ice and snow) clearly listed in Appendix C of "GB / T 36672-2018 Technical Requirements for Remote Service and Management System of Electric Vehicles." Road condition weighting calculations can be performed to improve the robustness of range prediction, make the range prediction closer to actual driving conditions, and reduce range prediction errors.

[0012] Furthermore, the driving behavior data includes braking frequency, number of rapid accelerations, and average throttle opening within a preset time period; S2 determines the driving behavior weights based on the driving behavior data, specifically including: Based on the braking frequency, a braking frequency coefficient is determined; based on the number of rapid accelerations, a rapid acceleration frequency coefficient is determined; and based on the average throttle opening, a throttle opening coefficient is determined. The driving behavior weights are determined based on the braking frequency coefficient, the rapid acceleration frequency coefficient, and the throttle opening coefficient.

[0013] Based on the aforementioned technical methods, driving behavior is refined into three quantifiable indicators: braking frequency, number of rapid accelerations, and average throttle opening. Driving behavior weights are then constructed based on these indicators, achieving a precise characterization of the key variable of "driver." This significantly improves the individual adaptability and accuracy of range and revenue predictions.

[0014] Furthermore, S4 specifically includes: When the commercial vehicle is a pure electric vehicle, the total weight is determined based on the road condition weight, the driving behavior weight, and the load weight. The energy consumption data of the commercial vehicle is determined based on the calibrated base mileage of the commercial vehicle, the total weight, and the preset power consumption rate.

[0015] Furthermore, S4 specifically includes: When the commercial vehicle is a hybrid vehicle, the total weight is determined based on the road condition weight, the driving behavior weight, and the load weight. The energy consumption data of the commercial vehicle is determined based on the calibrated base mileage, the total weight, and the preset energy consumption rate; and the fuel consumption data of the commercial vehicle is determined based on the base mileage, the total weight, the preset fuel consumption rate, and the preset engine operating condition coefficient. Based on the electricity consumption data and the fuel consumption data, the energy consumption data of the commercial vehicle is determined.

[0016] Furthermore, the vehicle status data includes the remaining battery power of the commercial vehicle; S5 specifically includes: Based on the energy consumption data and the remaining battery power, it is determined whether the commercial vehicle's range meets the freight journey requirements.

[0017] Furthermore, the vehicle status data includes the remaining battery power and remaining fuel level of the commercial vehicle; S5 specifically includes: The remaining total energy is determined based on the remaining electricity and the remaining fuel. Based on the energy consumption data and the remaining total energy, it is determined whether the commercial vehicle's range meets the freight journey requirements.

[0018] Furthermore, in S6, the revenue of the freight trip is determined based on the energy consumption data and freight information, specifically including: Based on the energy consumption data, determine the electricity cost; and based on the road information, determine the highway toll corresponding to the travel route; The revenue of the freight trip is determined based on the freight information, the electricity cost, the preset fixed costs, and the highway toll.

[0019] Furthermore, in S6, the revenue of the freight trip is determined based on the energy consumption data and freight information, specifically including: Based on the electricity consumption data, determine the electricity cost; based on the fuel consumption data, determine the fuel cost; and based on the road information, determine the highway toll corresponding to the travel route. The revenue of the freight trip is determined based on the freight information, electricity costs, fuel costs, preset fixed costs, and highway tolls.

[0020] Furthermore, S3 specifically includes: Based on the road information corresponding to the travel route, the travel route is divided into segments to determine each route segment; For each route segment, the road condition weight, driving behavior weight, and load weight are determined based on the road information, load information, and driving behavior data corresponding to that route segment.

[0021] Based on the aforementioned technical methods, the entire journey is no longer treated as a single operating condition. Instead, it is divided into several homogeneous route segments according to road information (such as changes in gradient, road surface type switching, weather zone boundaries, and the start and end points of congested road sections). This makes the range prediction and benefit calculation results closer to the dynamic changes during actual driving, significantly improving the reliability of range judgment and the precision of benefit assessment, and avoiding underestimation or overestimation caused by "averaging".

[0022] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: This solution accurately calculates the required range for freight trips, taking into account factors such as load capacity, road conditions, and driving style. Furthermore, it allows drivers to anticipate the profitability of their trip and whether the vehicle's range is sufficient before departure, improving driver efficiency. The onboard display visually presents information such as "delivery capability," "estimated revenue," and "energy consumption details," reducing driver cognitive burden and improving system usability. It achieves high-precision range assessment and economic benefit prediction for single freight missions of commercial vehicles, combining technological advancement with commercial practicality. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this specification and form part of this specification, illustrate exemplary embodiments and are used to explain this specification, but do not constitute an undue limitation thereof. In the drawings: Figure 1 A flowchart illustrating a method for predicting the range and revenue of a commercial vehicle, provided as an embodiment of this specification; Figure 2 This is a schematic diagram of a trip estimation page provided in this instruction manual; Figure 3 This specification provides a corresponding Figure 1 A schematic diagram of the structure of an electronic device. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0025] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0026] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0027] Figure 1 A flowchart illustrating a method for predicting the range and revenue of a commercial vehicle, as provided in this embodiment of the specification, includes the following steps: S1: In response to a prediction request from a driver in a commercial vehicle, obtain the destination, load information, freight information, vehicle status data, and driving behavior data of the freight trip input by the driver.

[0028] In this specification, the process of predicting the range and revenue of commercial vehicles is described. In the embodiments described herein, this process can be executed by a server. However, this specification does not limit the type of device or platform used to perform this process. For example, a personal computer, mobile terminal, in-vehicle infotainment system, or an in-vehicle (or vehicle-mounted) Electronic Control Unit (ECU) can also be used. For ease of description, the following explanation uses an in-vehicle infotainment system as the executing entity to describe the method for predicting the range and revenue of commercial vehicles.

[0029] In one or more embodiments of this specification, the vehicle-mounted infotainment system in the commercial vehicle responds to a driver's prediction request. The driver can manually input the destination and load information of the freight trip on a display device in the commercial vehicle (such as a central control screen) or a mobile application wirelessly connected to the vehicle-mounted infotainment system to complete sending a prediction request to the system. The system or mobile application (such as a freight-related application) will then automatically provide the freight cost information for this trip. Therefore, the vehicle-mounted infotainment system can determine the destination and load information of the freight trip input by the driver, as well as the automatically calculated freight cost. Of course, the system can also acquire vehicle status data and driver behavior data from the vehicle's in-vehicle bus or sensors.

[0030] S2: Based on the destination, determine the travel route and the road information corresponding to the travel route from the map data unit.

[0031] In one or more embodiments of this specification, the vehicle-mounted system can determine the route and corresponding road information from the map data unit based on the destination of the freight trip. The map data unit can automatically provide the route from the current location of the commercial vehicle to the destination. Furthermore, the map data unit also stores the road information corresponding to the route.

[0032] Road information may include: 1. Slope: -10°~10° (data collected in 0.1° increments).

[0033] 2. Road surface types: asphalt, cement, dirt road, ice and snow.

[0034] 3. Road weather data (rainfall): 0~50mm / h.

[0035] 4. Road traffic density: 0~100 vehicles / km.

[0036] 5. Number of traffic lights: per 10km.

[0037] S3: Determine the load weight, road condition weight, and driving behavior weight based on the load information, road information, and driving behavior data.

[0038] In one or more embodiments of this specification, the vehicle-mounted system can determine the load weight, road condition weight, and driving behavior weight based on the load information of the freight trip, the road information corresponding to the trip route, and the driver's driving behavior data.

[0039] Specifically, when road information includes the gradient of the route, road surface type, road weather data, and road traffic density, the vehicle-mounted system can determine the gradient energy efficiency coefficient based on the gradient of the route; the road surface energy consumption coefficient based on the road surface type; the rainfall energy consumption coefficient based on the road weather data; and the traffic density energy consumption coefficient based on the road traffic density.

[0040] The following methods can be used to determine the slope energy efficiency coefficient, pavement energy consumption coefficient, rainfall energy consumption coefficient, and traffic density energy consumption coefficient: Slope energy consumption coefficient: -10°~-3°=0.85, -3°~3°=1.0, 3°~8°=1.15, >8°=1.25.

[0041] Road surface energy consumption coefficient: asphalt = 0.95, cement = 1.0, dirt road = 1.2, ice and snow = 1.5.

[0042] Rainfall energy consumption coefficient: Sunny day = 1.0, light rain (<5mm / h) = 1.08, moderate rain (5-15mm / h) = 1.15, heavy rain (>15mm / h) = 1.25.

[0043] Traffic density energy consumption coefficient: Smooth traffic (<20 vehicles / km) = 1.0, Mild traffic (20-50 vehicles / km) = 1.12, Congested traffic (>50 vehicles / km) = 1.3.

[0044] Then, the vehicle system can determine the road condition weight W1 based on the gradient energy efficiency coefficient, road surface energy consumption coefficient, rainfall energy consumption coefficient, and traffic density energy consumption coefficient.

[0045] W1 can be calculated in the following ways: W1 = [(Slope energy consumption coefficient × 0.3) + (Road surface energy consumption coefficient × 0.25) + (Rainfall energy consumption coefficient × 0.2) + (Traffic density energy consumption coefficient × 0.25)] × 0.4 In this manual, when driving behavior data includes at least the braking frequency, number of rapid accelerations, and average throttle opening within a preset time period (e.g., one month), the vehicle system can determine a braking frequency coefficient based on the braking frequency, a rapid acceleration frequency coefficient based on the number of rapid accelerations, and a throttle opening coefficient based on the average throttle opening. Of course, driving behavior data is not limited to the above three types; it may also include steering wheel angle, accelerator position, brake pressure, etc. This manual only uses braking frequency, number of rapid accelerations, and average throttle opening as examples; specific driving behavior data can be set and obtained according to actual conditions.

[0046] The methods for determining the braking frequency coefficient, the rapid acceleration frequency coefficient, and the throttle opening coefficient can be as follows: Braking frequency coefficient = 1 + 0.005 * (times / 10km), which means that every additional braking time of 10km increases energy consumption by about 0.5%.

[0047] The coefficient for the number of rapid accelerations is 1 + 0.015 (times / 10km), which means that for every additional rapid acceleration every 10km, energy consumption increases by approximately 1.5%.

[0048] Throttle opening coefficient: average throttle opening < 30% = 1.0, 30% ≤ average throttle opening ≤ 60% = 1.05~1.15 (the specific value can be determined by linear interpolation based on the average throttle opening), average throttle opening > 60% = 1.15~1.30 (the specific value can be determined by linear interpolation based on the average throttle opening).

[0049] Then, the vehicle system can determine the driving behavior weight W2 based on the braking frequency coefficient, the rapid acceleration frequency coefficient, and the throttle opening coefficient.

[0050] W2 can be calculated in the following ways: W2 = [(Throttle opening coefficient × 0.4) + (Braking frequency coefficient × 0.3) + (Rapid acceleration frequency coefficient × 0.3)] × 0.25 In this manual, the vehicle infotainment system can determine the load weight W3 based on the load information.

[0051] W3 can be calculated in the following ways: W3 = (1 + 0.08 × actual load) × 0.35 S4: Determine the energy consumption data of the commercial vehicle based on the vehicle type, road condition weight, driving behavior weight, and load weight.

[0052] In one or more embodiments of this specification, the vehicle-mounted system can determine the energy consumption data of the commercial vehicle based on the vehicle type, road condition weight, driving behavior weight, and load weight.

[0053] Specifically, when the commercial vehicle is a pure electric vehicle, the vehicle-mounted system determines the total weight based on road condition weight, driving behavior weight, and load weight, i.e., total weight = W1 + W2 + W3. The vehicle-mounted system then determines the energy consumption data Q of the commercial vehicle based on the vehicle's calibrated base mileage, the total weight, and the preset energy consumption rate.

[0054] Q is calculated as follows: Q = Base mileage L × Energy consumption rate × (W1 + W2 + W3) Of course, in this manual, when the commercial vehicle is a hybrid vehicle, the vehicle system can also determine the total weight based on road condition weight, driving behavior weight, and load weight, where the total weight = W1 + W2 + W3. Then, based on the commercial vehicle's calibrated base mileage L, the total weight, and the preset energy consumption rate, the commercial vehicle's energy consumption data Q' is determined. And based on the base mileage L, the total weight, the preset fuel consumption rate, and the preset engine operating condition coefficient, the commercial vehicle's fuel consumption data F is determined.

[0055] The calculation methods for Q' and F are as follows: Q' = L × power consumption rate × (W1 + W2 + W3) F = L × fuel consumption rate × (W1 + W2 + W3) × 1.2 (i.e., engine operating coefficient) Finally, the vehicle's infotainment system determines the commercial vehicle's energy consumption data based on electricity and fuel consumption data.

[0056] S5: Based on the energy consumption data and the vehicle status data, determine whether the commercial vehicle's range meets the freight trip requirements. If yes, proceed to step S6. If no, proceed to step S7.

[0057] In one or more embodiments of this specification, the vehicle-mounted system can determine whether the commercial vehicle's range meets the freight trip requirements based on energy consumption data and vehicle status data. If yes, then step S6 is executed. If no, then step S7 is executed.

[0058] Specifically, when the commercial vehicle is a pure electric vehicle, the vehicle status data may include information such as the remaining battery power and battery temperature. The vehicle's infotainment system uses this energy consumption data and remaining battery power to determine whether the commercial vehicle's range is sufficient for freight trips.

[0059] At this point, the commercial vehicle's range is Q / energy consumption rate.

[0060] For example, if Q ≤ remaining power × 0.9 (10% emergency power reserved), then the commercial vehicle's range is sufficient for freight trips; otherwise, it is not.

[0061] In this manual, when the commercial vehicle is a hybrid vehicle, the vehicle status data includes the remaining battery power, remaining fuel level, battery temperature, engine speed, and other data.

[0062] The vehicle's infotainment system determines the total remaining energy based on the remaining battery power and fuel level. Then, based on the energy consumption data and the remaining total energy, it judges whether the commercial vehicle's range is sufficient for freight transport.

[0063] The total range S at this point is calculated as: (Q' / energy consumption rate) + (F / fuel consumption rate) Specifically, the remaining battery power and power consumption data can be compared to determine if the remaining battery power meets the power consumption data. The remaining fuel level and fuel consumption data can be compared to determine if the remaining fuel level meets the fuel consumption data. If both meet the requirements, proceed to step S6; otherwise, proceed to step S7.

[0064] S6: Based on the energy consumption data, the freight cost information, and the road information, determine the revenue of the freight trip, and display a trip estimation page through the display device on the commercial vehicle. The content displayed on the estimation page includes at least the revenue, the energy consumption data, and text information indicating that the freight trip can reach the destination.

[0065] In one or more embodiments of this specification, the vehicle-mounted system can determine the revenue of a freight trip based on energy consumption data, freight information, and road information. The freight information can come from the order data unit of this freight trip, and the data may include basic freight costs, loading and unloading time, highway tolls (related to the map data unit), etc.

[0066] Specifically, when the commercial vehicle is a pure electric vehicle, the vehicle's infotainment system can determine electricity costs based on energy consumption data, and determine highway tolls for the route based on road information. Then, based on freight information, electricity costs, preset fixed costs, and highway tolls, the revenue P for the freight trip is determined.

[0067] Revenue P = Base freight - [Q × Electricity cost + Fixed costs (depreciation approximately 150 RMB / trip, can be calculated based on actual conditions) + Additional expenses such as highway tolls] When the commercial vehicle is a hybrid, the vehicle's infotainment system determines electricity costs based on electricity consumption data, fuel costs based on fuel consumption data, and highway tolls for the route based on road information. Finally, based on freight information, electricity costs, fuel costs, preset fixed costs, and highway tolls, the revenue P for the freight trip is determined.

[0068] Revenue P = Basic freight cost - [Q' × Electricity cost + F × Fuel cost + Fixed costs (depreciation approximately 150 RMB / trip, can be calculated based on actual conditions) + Additional expenses such as highway tolls] Finally, the vehicle-mounted infotainment system can display a trip prediction page on the commercial vehicle's display device. This page should include at least revenue (P), energy consumption data, and text information indicating the possible destinations for the freight trip. It can also provide helpful tips to the driver, such as encouraging smooth driving.

[0069] Figure 2 This is a schematic diagram of a trip estimation page provided in this manual. As shown, the display device shows the trip estimation page, including reachable text information and estimated energy consumption (i.e., energy consumption data). It also displays the estimated revenue for this order. Additionally, there are helpful tips for the driver, advising them to drive smoothly and switch to gasoline to maintain power when the hybrid vehicle's battery SOC (State of Charge) is ≤20%.

[0070] S7: Display a prompt message to the driver via the display device, the prompt message being used to inform the driver that the commercial vehicle's range does not meet the freight trip requirements.

[0071] In one or more embodiments of this specification, the vehicle-mounted system can display a prompt message to the driver via a display device. This prompt message indicates that the commercial vehicle's range is insufficient for freight trips and that the vehicle needs to be charged or refueled. Of course, even when the commercial vehicle's range is insufficient for freight trips, the display device can also show the driver predicted revenue or energy consumption data.

[0072] based on Figure 1This paper presents a method for predicting the range and revenue of commercial vehicles. This solution accurately calculates the required range for a freight trip, taking into account factors such as load, road conditions, and driving style. Furthermore, it allows drivers to anticipate the revenue and vehicle range adequacy of their trip before departure, improving driver efficiency. The onboard display intuitively presents information such as "delivery capability," "estimated revenue," and "energy consumption details," reducing driver cognitive burden and improving system usability. This method achieves high-precision range assessment and economic benefit prediction for single freight missions of commercial vehicles, combining technological advancement with commercial practicality.

[0073] An example of an implementation described using a hybrid electric vehicle: A driver is driving a 4.5-ton hybrid truck to pick up an order from the platform (logistics park in location A to warehouse in location B, freight cost 1,500 yuan, highway toll 100 yuan).

[0074] The vehicle's infotainment system can acquire high-precision map data: Route 1 (130km highway, 3° gradient, concrete road surface, rainfall 2mm / h (light rain), traffic density 30 vehicles / km (slow traffic), 3 traffic lights / 10km). Vehicle status data: SOC=30% (remaining battery 24kWh), remaining fuel 30L, engine speed 1500rpm. Driving behavior data: 2 instances of rapid acceleration / 10km in the past month (smooth driving). Load data: Identifies a cargo manifest weight of 4 tons (within the national standard range of 0-50 tons). Order data: Freight cost 1500 yuan, loading and unloading time 1 hour, highway toll 100 yuan.

[0075] Then calculate the road condition weight W1: Slope energy consumption coefficient = 1.15 (3°), pavement coefficient = 1.0 (cement), rainfall coefficient = 1.08 (light rain), traffic flow coefficient = 1.12 (congestion mitigation). W1 = [(1.15×0.3)+(1.0×0.25)+(1.08×0.2)+(1.12×0.25)] × 0.4 =[0.345+0.25+0.216+0.28]× 0.4 = 1.091×0.4 = 0.4364 Driving behavior weight W2: Rapid acceleration frequency coefficient = 1.03 (2 times / 10km), throttle opening coefficient = 1.0, braking frequency coefficient = 1.0 W2 = [(1.0×0.4)+(1.0×0.3)+(1.03×0.3)] × 0.25 = 0.25225 Load Capacity W3: Load factor = 1 + 0.08 × 4 = 1.32 W3 = 1.32 × 0.35 = 0.462 Total weight = 0.4364 + 0.25225 + 0.462 = 1.15065 Energy consumption calculation: Q=130km×0.5kWh / km×1.15065=74.79225kWh (That is, 74.79225 - 24 = 50.79225 kWh needs to be charged) F = 130km × 0.1L / km × 1.15065 × 1.2 (engine operating coefficient) = 17.95014kL Battery life assessment: Total range S = (24kWh / 0.5kWh / km) + (30L / 0.1L / km) = 48km + 300km = 348km > 130km × 1.1 = 143km, therefore, it is determined that the route is "reachable".

[0076] Profit Calculation: P = 1500 - [74.79225 × 1.2 (electricity cost) + 17.95014 × 8 (oil price) + 150 (depreciation) + 100 (highway toll)] = 1500 - 483.35182 ≈ 1016.65 yuan.

[0077] Furthermore, in one or more embodiments of this specification, the vehicle-mounted system can also segment the travel route according to the road information corresponding to the travel route to determine each route segment. Then, for each route segment, based on the road information, load information, and driving behavior data corresponding to that route segment, the system determines the road condition weight, driving behavior weight, and load weight corresponding to that route segment.

[0078] For example, the driving route is divided into n segments, and the weight of each segment (segment 1 to segment n) is calculated separately as follows: W1i = [(Segment i slope energy consumption coefficient × 0.3) + (Segment i road surface energy consumption coefficient × 0.25) + (Segment i rainfall energy consumption coefficient × 0.2) + (Segment i traffic density energy consumption coefficient × 0.25)] × 0.4 W2i = Driving behavior weight (Note: Driving habits are a global attribute of the driver, uniform throughout the entire segment, and do not need to be calculated in segments) W3i = Load weight (Note: Load is a whole vehicle attribute, uniform for the entire segment, and does not need to be calculated segment by segment) Total local weight of segment i = W1i + W2i + W3i Then, Qi (i=1~N) is calculated as follows: Qi = Li × energy consumption rate × total weight of segment i. If it is a hybrid vehicle, then Fi = Li × fuel consumption rate × total weight of segment i × 1.2 (engine coefficient). Total energy consumption = ΣQi + ΣFi.

[0079] It's worth noting that the vehicle-mounted system segments the route based on road information, automatically splitting the A / B path using the "road attribute boundaries" of a high-precision map based on map data units. Segmentation is triggered when the road surface type changes (e.g., from cement to mud), the slope changes by more than 3°, or the traffic density changes by more than 20 vehicles / km. The minimum segment length is 500m (to avoid computational redundancy due to excessive length) and the maximum is 5km (to avoid errors due to excessive length). Each segment is assigned a unique identifier (e.g., "Segment 1-A→A1", "Segment 2-A1→A2") for easy subsequent tracking. Furthermore, it can collect data individually for each segment based on the segment identifier, including road surface type, slope, number of traffic lights within the segment, average traffic density, and real-time weather for that segment (e.g., Segment 1 sunny, Segment 2 light rain).

[0080] In this manual, W1, W2, W3, and energy consumption data calculated from multiple (e.g., 10,000) freight trips of commercial vehicles can also be used as training data to train a Long Short-Term Memory (LSTM) model, with energy consumption per unit mile (including electricity consumption and fuel consumption per unit mile) as the model output. Furthermore, since new energy commercial vehicles include pure electric and hybrid models, training data from 5,000 pure electric trips and 5,000 hybrid trips can be obtained separately. Model training can be used to verify the rationality of the weights agreed upon based on big data. For example, for pure electric vehicles, based on 5,000 tests on different roads, the weights of W1, W2, and W3, along with energy consumption data, can be submitted according to the agreed coefficients and calculated using the described formulas to obtain the actual state of the vehicle and whether there are excessive errors in the coefficients, thus allowing for adjustments to the various coefficients.

[0081] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 This paper presents a method for predicting the range and revenue of commercial vehicles.

[0082] This instruction manual also provides Figure 3 The diagram shows a schematic structural representation of the electronic device. Figure 3As shown, at the hardware level, this electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above. Figure 1 This paper presents a method for predicting the range and revenue of commercial vehicles.

[0083] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0084] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0085] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0086] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0087] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0088] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0093] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0094] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0096] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0098] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0099] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for predicting the range and revenue of commercial vehicles, characterized in that, include: S1. In response to the prediction request from the driver in the commercial vehicle, obtain the destination, load information, freight information, vehicle status data and driving behavior data of the freight trip input by the driver; S2. Based on the destination, determine the travel route and the road information corresponding to the travel route from the map data unit; S3. Determine the load weight, road condition weight, and driving behavior weight based on the load information, road information, and driving behavior data; S4. Determine the energy consumption data of the commercial vehicle based on the vehicle type, road condition weight, driving behavior weight, and load weight; S5. Based on the energy consumption data and the vehicle status data, determine whether the commercial vehicle's range meets the freight journey requirements; S6. If so, the revenue of the freight trip is determined based on the energy consumption data, the freight cost information and the road information, and a trip estimation page is displayed on the display device on the commercial vehicle. The content displayed on the estimation page includes at least the revenue, the energy consumption data and text information indicating that the freight trip can reach the destination. S7. If not, then display a prompt message to the driver through the display device, the prompt message being used to remind the driver that the commercial vehicle's range does not meet the freight trip requirements.

2. The method for predicting the range and revenue of commercial vehicles as described in claim 1, characterized in that, The road information includes the gradient of the route, road surface type, road weather data, and road traffic density; S3 determines the road condition weights based on the road information, specifically including: Based on the slope of the route, determine the slope energy efficiency coefficient; based on the road surface type, determine the road surface energy consumption coefficient; based on the road weather data, determine the rainfall energy consumption coefficient; and based on the road traffic density, determine the traffic density energy consumption coefficient. The road condition weights are determined based on the slope energy efficiency coefficient, the road surface energy consumption coefficient, the rainfall energy consumption coefficient, and the traffic density energy consumption coefficient.

3. The method for predicting the range and revenue of commercial vehicles as described in claim 1, characterized in that, The driving behavior data includes braking frequency, number of rapid accelerations, and average throttle opening within a preset time period; S2 determines the driving behavior weights based on the driving behavior data, specifically including: Based on the braking frequency, a braking frequency coefficient is determined; based on the number of rapid accelerations, a rapid acceleration frequency coefficient is determined; and based on the average throttle opening, a throttle opening coefficient is determined. The driving behavior weights are determined based on the braking frequency coefficient, the rapid acceleration frequency coefficient, and the throttle opening coefficient.

4. The method for predicting the range and revenue of commercial vehicles as described in claim 1, characterized in that, S4 specifically includes: When the commercial vehicle is a pure electric vehicle, the total weight is determined based on the road condition weight, the driving behavior weight, and the load weight. The energy consumption data of the commercial vehicle is determined based on the calibrated base mileage of the commercial vehicle, the total weight, and the preset power consumption rate.

5. The method for predicting the range and revenue of commercial vehicles as described in claim 1, characterized in that, S4 specifically includes: When the commercial vehicle is a hybrid vehicle, the total weight is determined based on the road condition weight, the driving behavior weight, and the load weight. The energy consumption data of the commercial vehicle is determined based on the calibrated base mileage, the total weight, and the preset energy consumption rate; and the fuel consumption data of the commercial vehicle is determined based on the base mileage, the total weight, the preset fuel consumption rate, and the preset engine operating condition coefficient. Based on the electricity consumption data and the fuel consumption data, the energy consumption data of the commercial vehicle is determined.

6. The method for predicting the range and revenue of commercial vehicles as described in claim 4, characterized in that, The vehicle status data includes the remaining battery power of the commercial vehicle; S5 specifically includes: Based on the energy consumption data and the remaining battery power, it is determined whether the commercial vehicle's range meets the freight journey requirements.

7. The method for predicting the range and revenue of commercial vehicles as described in claim 5, characterized in that, The vehicle status data includes the remaining battery power and remaining fuel of the commercial vehicle; S5 specifically includes: The remaining total energy is determined based on the remaining electricity and the remaining fuel. Based on the energy consumption data and the remaining total energy, it is determined whether the commercial vehicle's range meets the freight journey requirements.

8. The method for predicting the range and revenue of commercial vehicles as described in claim 6, characterized in that, S6 determines the revenue of the freight trip based on the energy consumption data and freight information, specifically including: Based on the energy consumption data, determine the electricity cost; and based on the road information, determine the highway toll corresponding to the travel route; The revenue of the freight trip is determined based on the freight information, the electricity cost, the preset fixed costs, and the highway toll.

9. The method for predicting the range and revenue of commercial vehicles as described in claim 7, characterized in that, S6 determines the revenue of the freight trip based on the energy consumption data and freight information, specifically including: Based on the electricity consumption data, determine the electricity cost; based on the fuel consumption data, determine the fuel cost; and based on the road information, determine the highway toll corresponding to the travel route. The revenue of the freight trip is determined based on the freight information, electricity costs, fuel costs, preset fixed costs, and highway tolls.

10. The method for predicting the range and revenue of commercial vehicles as described in claim 2, characterized in that, S3 specifically includes: Based on the road information corresponding to the travel route, the travel route is divided into segments to determine each route segment; For each route segment, the road condition weight, driving behavior weight, and load weight are determined based on the road information, load information, and driving behavior data corresponding to that route segment.

Images