Method for optimizing measurement and calculation of endurance mileage of fuel vehicle under different working conditions

Abstract

The invention discloses a method for optimizing endurance mileage measurement and calculation of a fuel vehicle under different working conditions, which comprises the following steps: collecting big data of real-time vehicle speed, instantaneous fuel consumption and vehicle vibration frequency in the driving process of the fuel vehicle, keeping the time consistency of the real-time vehicle speed, instantaneous fuel consumption and vehicle vibration frequency data, and establishing a road condition model; according to the real-time vehicle speed and the vehicle-mounted vibration frequency data, preset road working condition parameters in road condition models are matched, and the driving state of the fuel vehicle is matched into the corresponding road condition models; calculating fuel consumption estimation amount per unit mileage of the fuel vehicle under the working condition based on the instantaneous fuel consumption and a matched road condition model; and the real-time endurance mileage is obtained by combining the fuel consumption estimation amount per unit mileage and the vehicle-mounted residual fuel amount. The problem that the endurance mileage of the fuel vehicle is not accurate under different working conditions is solved, and a driver can conveniently control the driving distance of the vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of cruising range calculation for fuel vehicles, and in particular to a method for optimizing cruising range calculation for fuel vehicles under different operating conditions. Background Art

[0002] The driving range can also be called the cruising range, which refers to the total mileage that a vehicle or other driving tool can travel continuously under the current driving conditions using the current fuel reserve in the tank. The cruising range is a relatively important display content in the existing automobile instrument display system. Different road conditions will be encountered during driving, such as urban roads, mountain roads, and highways. The driving conditions under different road conditions are very different, which will lead to a large difference in energy consumption in different time periods. Most existing vehicles use the vehicle's instantaneous fuel consumption and the remaining fuel in the tank to calculate the cruising range. However, the instantaneous fuel consumption fluctuates greatly, which will result in the inability to accurately display the current distance that can be continued in a single trip.

[0003] To this end, we have designed a method to optimize the calculation of the cruising range of fuel vehicles under different operating conditions to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcoming in the prior art that the existing automobile cannot accurately calculate the cruising range when driving under different road conditions, and proposes a method for optimizing the calculation of the cruising range under different working conditions of fuel vehicles, the purpose of which is to optimize the calculation strategy of the cruising range and improve the calculation data of the cruising range to be more in line with the vehicle driving environment and in line with the actual cruising range of the vehicle.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions, the method comprising the following steps: Step 1: collect the big data of the real-time speed, average fuel consumption and vehicle vibration frequency of the fuel vehicle during driving, maintain the time consistency of the real-time speed, average fuel consumption and vehicle vibration frequency data, and establish a road condition model; Step 2: match the preset road condition parameters in the road condition model according to the monitored real-time speed and vehicle vibration frequency data, and match the driving state of the fuel vehicle to the corresponding road condition model; Step 3, calculating the estimated fuel consumption per unit mileage of the fuel vehicle under the working condition based on the monitored instantaneous fuel consumption and the matched road condition model; Step 4: Combine the estimated fuel consumption per mileage with the remaining fuel on the vehicle to calculate the cruising range of the fuel vehicle under the road condition. Furthermore, in step 1, the real-time vehicle speed data is obtained by collecting the number of wheel rotations of the fuel vehicle per unit time, the vehicle vibration frequency data is measured based on the on-board laser displacement sensor, and the remaining fuel in the fuel tank is measured in real time through the on-board fuel sensor in the fuel tank to obtain the fuel consumption of the fuel vehicle. The average fuel consumption is calculated in the following way: Based on the actual fuel consumption per unit distance of a fuel vehicle, the calculation formula for the average fuel consumption of a fuel vehicle is obtained: In the above formula, Indicates the average fuel consumption of fuel vehicles. Indicates the fuel consumption of fuel vehicles; It represents the driving distance of a fuel vehicle, which is calculated based on the number of wheel rotations. The road condition model established based on the time consistency of the real-time vehicle speed, average fuel consumption and vehicle vibration frequency data includes an urban road model, a highway model and a mountain road model.

[0006] Furthermore, in step 2, according to the real-time vehicle speed and vehicle vibration frequency data, the preset road condition parameters are matched to the corresponding road condition model in the following manner: Compare the collected real-time vehicle speed and vehicle vibration frequency data with the set speed range and vehicle vibration frequency range in the road database; If the real-time vehicle speed falls within the set speed range or the vehicle vibration frequency data falls within the vehicle vibration frequency range, the road condition model of the corresponding working condition is retrieved; Selecting road section environment parameters matching an urban road model, a highway model or a mountain road model from a corresponding road condition model database; According to the road type in the road condition model matched by the fuel vehicle, the driving state of the fuel vehicle is matched to the corresponding road condition model.

[0007] Furthermore, the matching basis of the road condition model corresponding to the working condition is as follows: According to the speed limit requirements and the vehicle's bumping frequency as the basis for judgment, an urban road model, a highway model or a mountain road model is set in the road condition model; Select the speed limit critical value between the urban road model and the highway model, and set the speed limit critical value to V. When the collected real-time vehicle speed is less than V, it is determined that the fuel vehicle is traveling on the urban road; when the collected real-time vehicle speed is greater than or equal to V, it is determined that the fuel vehicle is traveling on the highway; When matching the road condition model, the mountain road model in the road condition model has a higher matching priority than the urban road model and the highway model. The vibration frequency of the fuel vehicle is monitored and matched first. If the vibration frequency of the fuel vehicle is matched to the mountain road model according to the monitoring, it is no longer determined whether the real-time speed of the fuel vehicle matches the urban road model or the highway model. A vibration frequency used to represent the degree of bumpiness of a fuel vehicle is selected as a critical value for representing a mountain road model. The critical value of the vibration frequency is set to f. If the collected vehicle vibration frequency data is greater than the critical value of the vibration frequency f, it is determined that the fuel vehicle is traveling on a mountain road; otherwise, it is traveling on a city road or a highway.

[0008] Furthermore, in step 3, the estimated unit mileage fuel consumption of the fuel vehicle under the working condition is calculated based on the monitored instantaneous fuel consumption and the matched road condition model. The instantaneous fuel consumption and real-time vehicle speed are input as input variables into the calculation unit of the matching road condition model, and the linear regression mapping algorithm is used to analyze the relationship between vehicle speed and fuel consumption. The calculation formula of the road condition model is as follows:

[0009] In the formula, Indicates the estimated fuel consumption per mileage under the current operating conditions. Indicates the monitored instantaneous fuel consumption, the amount of fuel injected by the injector during the measurement time. and They represent the average fuel consumption and adjustment factor under urban road conditions in the road condition model, and They represent the average fuel consumption and adjustment factor under highway conditions in the road condition model, and They represent the average fuel consumption and adjustment factor under mountain road conditions in the road condition model respectively; When the fuel vehicle is matched to the urban road model in the road condition model, and are all zero; when the fuel vehicle matches the highway model in the road condition model, and are all zero; when the fuel vehicle matches the mountain road model in the road condition model, and All are zero.

[0010] Furthermore, in step 4, the real-time cruising range is obtained in the following manner: The remaining fuel amount in the fuel tank is measured in real time by the on-board fuel level sensor in the fuel tank, and the remaining fuel amount in the fuel tank is obtained as the basic data point. The remaining fuel amount is calculated in combination with the estimated amount of fuel consumption per unit mileage under the condition of known remaining fuel amount, and the maximum cruising range of the fuel vehicle under the road condition is calculated. Compared with the prior art, the beneficial effects of the present invention are: the present invention collects various relevant parameters and combines them with the constructed road condition model, fully considering the differences in fuel consumption of fuel vehicles under various road conditions, overcoming the limitation of the prior art that the cruising range does not take into account different road conditions, and matches the road condition type in the road condition model according to the real-time vehicle speed and vibration frequency, fully considering the fuel consumption of fuel vehicles under different working conditions The method can be used to calculate the vehicle's cruising range and solve the problem of inaccuracy in calculating the cruising range of existing fuel vehicles based on instantaneous fuel consumption. The technical solution proposed by the method is based on the different fuel consumption characteristics of fuel vehicles under various road conditions. The calculated cruising range is closer to the actual vehicle driving scenario, which is convenient for the driver to control the vehicle's driving distance. The cruising range information can be updated in time, thereby helping to plan driving routes and decide where to stop at gas stations. It provides drivers with more reliable and trustworthy cruising range reference data, which can effectively alleviate driving anxiety caused by the inability to accurately judge the driving distance that can be supported by the remaining fuel, ensure driving safety, and reduce the probability of oil-free breakdown accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of a method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions proposed by the present invention. DETAILED DESCRIPTION

[0012] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "includes" do not exclude the existence of other identical elements in the process, method, article or device including the elements. The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments.

[0013] Next, the method of optimizing the mileage calculation of fuel vehicles under different working conditions of the present invention is described. First, in the actual application scenario, the system will start to collect a series of real-time data during the operation of the fuel vehicle through various sensors and devices of the vehicle. These include parameters such as the driving speed value of the fuel vehicle, the average fuel consumption reading, and the frequency of the bumps of the fuel vehicle. These big data are the basis for creating a road condition model that includes multiple driving states.

[0014] Secondly, the system will match the pre-set road condition classification standards and characteristics in the road condition model based on the collected driving data of fuel vehicles, such as the driving speed of fuel vehicles and the frequency of bumps of fuel vehicles, so as to identify the road category that best meets the driving conditions of fuel vehicles.

[0015] For example, if the vehicle is currently driving at 80 kilometers per hour on a flat road, this situation will be classified as a high-speed driving state or a simulation of a specific highway vehicle model, and the corresponding highway model in the road condition model will be called to calculate the fuel consumption and mileage under this driving behavior. This operation can help use data that is more in line with the current traffic conditions in subsequent calculations.

[0016] Subsequently, the selected specific road condition type is combined with the real-time fuel consumption under that road condition type, and based on the remaining fuel in the tank, a calculation method is used to estimate the fuel consumption of the fuel vehicle under that road condition type, and predict how far the fuel vehicle can continue to travel based on the remaining fuel in the tank.

[0017] Finally, the above estimated fuel consumption per mileage and the remaining fuel in the vehicle are used as the basis for determining the range of the fuel vehicle under this working condition. The driver can obtain the approximate distance that can be driven safely without refueling.

[0018] Next, refer to Figure 1 , a method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions of the present invention is described in detail, and the method mainly comprises the following steps: Step 1: Collect the big data of real-time speed, average fuel consumption and vehicle vibration frequency of fuel vehicles during driving, maintain the time consistency of real-time speed, average fuel consumption and vehicle vibration frequency data, and establish a road condition model. Step 2: According to the monitored real-time speed and vehicle vibration frequency data, match the preset road condition parameters in the road condition model, and match the driving state of the fuel vehicle to the corresponding road condition model.

[0019] Step three, based on the monitored instantaneous fuel consumption and the matching road condition model, calculate the estimated fuel consumption per unit mileage of the fuel vehicle under the working condition.

[0020] Step 4: Combine the estimated fuel consumption per unit mileage with the remaining fuel in the vehicle to calculate the range of the fuel vehicle under the road condition. Specifically, in step 1, the real-time vehicle speed data is obtained by collecting the number of wheel rotations of the fuel vehicle per unit time, and the vehicle vibration frequency data is measured by the on-board laser displacement sensor. The fuel consumption of fuel vehicles is calculated by measuring the remaining fuel in the fuel tank in real time through the on-board fuel sensor in the fuel tank, and the average fuel consumption is calculated in the following way: Based on the actual fuel consumption per unit distance of a fuel vehicle, the calculation formula for the average fuel consumption of a fuel vehicle is obtained: In the above formula, Indicates the average fuel consumption of fuel vehicles. Indicates the fuel consumption of fuel vehicles, according to the fuel level sensor in the fuel tank; Indicates the driving distance of a fuel vehicle, which is calculated based on the number of wheel rotations; After the three big data collected are transmitted to the on-board computer via the CAN bus, they are uploaded to the back-end big data platform for processing, and the information is synchronized to ensure the time consistency of the data. According to the time consistency of the real-time vehicle speed, average fuel consumption and vehicle vibration frequency data, a road condition model that can simulate fuel consumption under various road conditions is created based on the big data algorithm. In order to meet the needs of most driving road conditions, the established road condition models can include urban road models, highway models and mountain road models.

[0021] In the above step 2, the measured real-time vehicle speed and vehicle vibration frequency data are associated with the road condition model to establish association rules; according to the real-time vehicle speed and vehicle vibration frequency data, the preset road condition parameters are matched to the corresponding road condition model in the following manner: Compare the collected real-time vehicle speed and vehicle vibration frequency data with the set speed range and vehicle vibration frequency range in the road database.

[0022] If the real-time vehicle speed falls within the set speed range or the vehicle vibration frequency data falls within the vehicle vibration frequency range, the road condition model of the corresponding working condition is retrieved, and the road condition model of the corresponding working condition includes an urban road model, a highway model and a mountain road model.

[0023] The road section environmental parameters matching the urban road model, highway model or mountain road model are selected from the corresponding road condition model database (such as the speed or load limit requirements according to traffic regulations, the speed parameters are particularly needed in this embodiment).

[0024] According to the road type in the road condition model matched by the fuel vehicle, the driving state of the fuel vehicle is matched to the corresponding road condition model.

[0025] The matching basis of the road condition model corresponding to the working condition is as follows:

[0026] According to the speed limit requirements and the vehicle's bumping frequency as the basis for judgment, an urban road model, a highway model or a mountain road model is set in the road condition model; Select the speed limit critical value between the urban road model and the highway model, and set the speed limit critical value to V. When the collected real-time vehicle speed is less than V, it is determined that the fuel vehicle is traveling on the urban road; when the collected real-time vehicle speed is greater than or equal to V, it is determined that the fuel vehicle is traveling on the highway; In this embodiment, a speed limit requirement of 80km / h is used as the speed limit critical value for judging the urban road model and the highway model. When the speed of the fuel vehicle is greater than 80km / h, it is determined that the vehicle is traveling on a highway, and the corresponding fuel vehicle adopts the fuel consumption mode of the highway model; when the speed of the fuel vehicle is lower than or equal to 80km / h, it is determined that the vehicle is traveling on a city road, and the corresponding fuel consumption mode of the urban road model is adopted to estimate the fuel consumption.

[0027] For the automotive field, the frequency of vehicle body vibration is a very important parameter, which reflects the degree of vehicle body bumps per unit time. When driving on a flat road, slight vibration occurs, and the number of reciprocating vibrations per unit time is large, that is, the vibration frequency is large. However, when the vehicle is driving on a mountain road or a bumpy road, the vibration amplitude is large, and the number of reciprocating vibrations per unit time is small, that is, the vibration frequency is small. Therefore, collecting the vehicle body vibration frequency can reflect the operating status of the vehicle.

[0028] In this embodiment, a vibration frequency is selected to represent the degree of bumpiness of a fuel vehicle, and a critical value is selected to represent a mountain road model. The critical value of the vibration frequency is set to f. If the collected vehicle vibration frequency data is greater than the critical value of the vibration frequency f, it is determined that the fuel vehicle is traveling on a mountain road; otherwise, it is traveling on a city road or a highway.

[0029] For example, the critical value of the vibration frequency is selected as 20HZ. If the collected vehicle vibration frequency data is greater than 20HZ, it is determined that the fuel vehicle is traveling on a mountain road; otherwise, it is traveling on a city road or a highway.

[0030] It should be noted that when matching the road condition model, the mountain road model in the road condition model has a higher matching priority than the urban road model and the highway model. The vibration frequency of the fuel vehicle is monitored and matched first. If the vibration frequency of the fuel vehicle is matched to the mountain road model based on the monitoring, it is no longer determined whether the real-time speed of the fuel vehicle matches the urban road model or the highway model.

[0031] In the above step three, the estimated fuel consumption per unit mileage of the fuel vehicle under the operating condition is calculated based on the monitored instantaneous fuel consumption and the matched road condition model.

[0032] The instantaneous fuel consumption and real-time vehicle speed are input as input variables into the calculation unit of the matching road condition model, and the linear regression mapping algorithm is used to analyze the relationship between vehicle speed and fuel consumption. The calculation formula of the road condition model is as follows:

[0033] In the formula, Indicates the estimated fuel consumption per mileage under the current operating conditions. Indicates the monitored instantaneous fuel consumption, which is obtained by measuring the amount of fuel injected by the injector per unit time; and They represent the average fuel consumption and adjustment factor under urban road conditions in the road condition model, and They represent the average fuel consumption and adjustment factor under urban road conditions in the road condition model, and They respectively represent the average fuel consumption and adjustment factor under urban road conditions in the road condition model.

[0034] When the fuel vehicle is matched to the urban road model in the road condition model, and are all zero; when the fuel vehicle matches the highway model in the road condition model, and are all zero; when the fuel vehicle matches the mountain road model in the road condition model, and All are zero.

[0035] The acquisition of the average fuel consumption and adjustment factor under each operating condition in the above-mentioned road condition model is based on the road condition model created by the big data collected in the early stage. The average fuel consumption under each operating condition in the road condition model is the average fuel consumption data obtained from the long-term driving of fuel vehicles on urban roads, highways and mountain roads, and the adjustment factor is to match the individual fuel consumption differences of fuel vehicles driving under the road conditions. The initial adaptation of the adjustment factor adjusts the comprehensive average fuel consumption of the vehicle.

[0036] In the above step 4, the real-time cruising range is obtained in the following way: The remaining fuel amount in the fuel tank is measured in real time by the on-board fuel level sensor in the fuel tank, and the remaining fuel amount in the fuel tank is obtained as the basic data point. The maximum feasible distance that can be covered under the condition of known remaining fuel amount is calculated based on the estimated fuel consumption per unit mileage, which is the measured cruising range.

[0037] For example, a compact A-class car with self-priming engine is driving on a city road. Based on the real-time vehicle speed and vehicle vibration frequency data monitored, the system determines that the car is suitable for the city road model. The system automatically matches the city road model to calculate the unit mileage fuel consumption of the fuel car under this condition. Calculate the instantaneous fuel consumption of the vehicle The average fuel consumption of the vehicle under this road condition is 6.4L / 100km, which is close to the average fuel consumption of the vehicle under this road condition. is 6.7L / 100km, then the initial stage adjustment factor is, The value is about 0.0448. =6.70016L / 100km. At this time, the remaining fuel volume in the vehicle's fuel tank is 32.4 liters. Based on the vehicle's instantaneous fuel consumption The calculated vehicle range is approximately 506 miles, while the actual estimated range under the road conditions is 484 kilometers. The range of 484 kilometers calculated according to the urban road model in the road condition model is 22 kilometers less than the original vehicle's range calculated based on instantaneous fuel consumption.

[0038] This embodiment can timely update the cruising range information through the above-mentioned calculation method, and based on the cruising range that the current remaining fuel can provide, it can help plan the driving route and decide the location of the gas station stop. In general, this method collects various relevant parameters and combines them with the constructed road condition model, fully taking into account the differences in fuel consumption of fuel vehicles under various road conditions, overcomes the limitations of the existing technology that the cruising range does not take into account different road conditions, and provides drivers with more reliable and trustworthy cruising range reference data. This is particularly critical for vehicles to provide accurate driving information on complex roads, especially in long-distance self-driving or emergency evacuation situations. It can effectively alleviate the driving anxiety caused by the inability to accurately judge the driving distance that the remaining fuel can support, and at the same time enhances the driver's ability to plan fuel filling in advance, further ensuring driving safety and reducing the probability of oil-free breakdown accidents.

[0039] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions, characterized in that: The method comprises the following steps: Step 1: Collect the big data of real-time speed, average fuel consumption and vehicle vibration frequency of the fuel vehicle during driving, maintain the time consistency of the real-time speed, average fuel consumption and vehicle vibration frequency data, and establish a road condition model; Step 2: According to the monitored real-time vehicle speed and vehicle vibration frequency data, the preset road condition parameters in the road condition model are matched, and the driving state of the fuel vehicle is matched to the corresponding road condition model; Step 3, calculating the estimated fuel consumption per unit mileage of the fuel vehicle under the working condition based on the monitored instantaneous fuel consumption and the matched road condition model; Step 4: Calculate the range of the fuel vehicle under the road conditions by combining the estimated fuel consumption per unit mileage with the remaining fuel on board.

2. The method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions according to claim 1 is characterized in that: In step 1, the real-time vehicle speed data is obtained by collecting the number of rotations of the wheels of the fuel vehicle per unit time, and the vehicle vibration frequency data is measured by the vehicle-mounted laser displacement sensor. The remaining fuel in the fuel tank is measured in real time through the on-board fuel sensor in the fuel tank to obtain the fuel consumption of the fuel vehicle. The average fuel consumption is calculated in the following way: Based on the actual fuel consumption per unit distance of a fuel vehicle, the calculation formula for the average fuel consumption of a fuel vehicle is obtained: In the above formula, Indicates the average fuel consumption of fuel vehicles. Indicates the fuel consumption of fuel vehicles; Indicates the driving distance of a fuel vehicle, which is calculated based on the number of wheel rotations; The road condition models established according to the time consistency of real-time vehicle speed, average fuel consumption and vehicle vibration frequency data include urban road models, highway models and mountain road models.

3. The method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions according to claim 1 is characterized in that: In step 2, according to the real-time vehicle speed and vehicle vibration frequency data, the preset road condition parameters are matched to the corresponding road condition model in the following manner: Compare the collected real-time vehicle speed and vehicle vibration frequency data with the set speed range and vehicle vibration frequency range in the road database; If the real-time vehicle speed falls within the set speed range or the vehicle vibration frequency data falls within the vehicle vibration frequency range, the road condition model of the corresponding working condition is retrieved; Selecting road section environment parameters matching an urban road model, a highway model or a mountain road model from a corresponding road condition model database; According to the road type in the road condition model matched by the fuel vehicle, the driving state of the fuel vehicle is matched to the corresponding road condition model.

4. The method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions according to claim 3 is characterized in that: The matching basis of the road condition model corresponding to the working condition is as follows: According to the speed limit requirements and the vehicle's bumping frequency as the basis for judgment, an urban road model, a highway model or a mountain road model is set in the road condition model; Select the speed limit critical value between the urban road model and the highway model, and set the speed limit critical value to V. When the collected real-time vehicle speed is less than V, it is determined that the fuel vehicle is traveling on the urban road; when the collected real-time vehicle speed is greater than or equal to V, it is determined that the fuel vehicle is traveling on the highway; When matching the road condition model, the mountain road model in the road condition model has a higher matching priority than the urban road model and the highway model. The vibration frequency of the fuel vehicle is monitored and matched first. If the vibration frequency of the fuel vehicle is matched to the mountain road model according to the monitoring, it is no longer determined whether the real-time speed of the fuel vehicle matches the urban road model or the highway model. A vibration frequency used to represent the degree of bumpiness of a fuel vehicle is selected as a critical value for representing a mountain road model. The critical value of the vibration frequency is set to f. If the collected vehicle vibration frequency data is greater than the critical value of the vibration frequency f, it is determined that the fuel vehicle is traveling on a mountain road; otherwise, it is traveling on a city road or a highway.

5. The method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions according to claim 1 is characterized in that: In step 3, the estimated fuel consumption per unit mileage of the fuel vehicle under the working condition is calculated based on the monitored instantaneous fuel consumption and the matching road condition model. The instantaneous fuel consumption and real-time vehicle speed are input as input variables into the calculation unit of the matching road condition model, and the linear regression mapping algorithm is used to analyze the relationship between vehicle speed and fuel consumption. The calculation formula of the road condition model is as follows:

6. In the formula, Indicates the estimated fuel consumption per mileage under current operating conditions. Indicates the monitored instantaneous fuel consumption, measuring the amount of fuel injected by the injector per unit time. and Respectively represent the average fuel consumption and adjustment factor under urban road conditions in the road condition model and They represent the average fuel consumption and adjustment factor under highway conditions in the road condition model, and They represent the average fuel consumption and adjustment factor under mountain road conditions in the road condition model respectively; when the fuel vehicle is matched to the urban road model in the road condition model, and are all zero; when the fuel vehicle matches the highway model in the road condition model, and are all zero; when the fuel vehicle matches the mountain road model in the road condition model, and All are zero.

7. The method for optimizing the calculation of the cruising range of a fuel vehicle under different working conditions according to claim 1 is characterized in that: In step 4, the real-time mileage is obtained in the following way: The remaining fuel amount in the fuel tank is measured in real time through the on-board fuel level sensor in the fuel tank, and the remaining fuel amount in the fuel tank is obtained as the basic data point. Combined with the estimated unit mileage fuel consumption, the maximum cruising range of the fuel vehicle under the road condition is calculated under the condition of known remaining fuel amount.

Images