A method and device for measuring the remaining fuel quantity and evaluating the cruising range of a motorcycle

Abstract

The application provides a motorcycle residual oil quantity and endurance mileage evaluation method and device, and the method comprises the following steps: firstly, calibrating the mailbox of the motorcycle; then, performing principal component analysis and clustering on the driving data of the motorcycle to extract several typical driving conditions; then, calculating the residual oil quantity of the motorcycle by fusing the collected speed, rotating speed, posture and fuel injection quantity; and finally, calculating the endurance mileage in combination with the driving conditions. The application significantly improves the accuracy of the calculation of the residual oil quantity and the endurance mileage of the motorcycle, and has great reference significance for the trip arrangement of the driver.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of motorcycle remaining fuel quantity detection, and particularly relates to a motorcycle remaining fuel quantity and cruising range evaluation method and device based on multi-sensor information fusion. BACKGROUND

[0002] The remaining fuel quantity in the motorcycle fuel tank is an important index for evaluating the cruising capability of the motorcycle and ensuring driving safety. Improving the precision of the fuel measuring system can enable the driver to obtain more accurate fuel information and directly determine the accuracy of the cruising range of the motorcycle. Accurate remaining fuel quantity and cruising range are of great significance for improving the safety of the driver, the efficiency of the vehicle and the user experience.

[0003] The driving conditions of a motorcycle are more extreme than those of a car, and there are a large number of sudden acceleration, deceleration and bending phenomena, which can cause the fuel level to shake violently and increase the difficulty of oil level detection. In addition, at the present stage, manufacturers use oil level sensors that are mostly float-type resistance sensors to adapt to the complex driving environment of motorcycles, improve the service life of the oil level sensor and reduce costs. However, the resolution of the segmented oil level sensor is low, and some discrete sensors can only provide a few discrete signals. Once the vehicle tilts, the value of the fuel sensor will be completely distorted. The traditional oil level algorithm only performs simple filtering on the signal of the oil level sensor, and is greatly affected by the shaking of the fuel level. The error of the calculated remaining fuel quantity is large, and there is currently no technology that can provide accurate remaining fuel quantity and cruising range information.

[0004] In summary, in order to overcome the problem of large calculation error of the existing remaining fuel detection and cruising range algorithm, it has become an urgent problem for technical personnel in the research field to invent a system and method that can provide accurate remaining fuel and cruising range. SUMMARY

[0005] In order to solve the above problems, the present application provides a motorcycle remaining fuel quantity and cruising range evaluation method and device based on attitude compensation and multi-source information fusion, the core idea of which is to reasonably utilize multiple information sources (fuel sensor oil level value, speed, vehicle speed, motorcycle attitude, instantaneous fuel injection amount output by the electronic injection system and some prior information, etc.) to improve the precision of the remaining fuel quantity and the cruising range.

[0006] The first aspect of the present application discloses a motorcycle remaining fuel detection and cruising range evaluation system, which comprises a collection module, a calibration module, a calculation module and a human-computer interaction module.

[0007] The data acquisition module is used for acquiring various sensor signals (including pulse signals, analog signals, switch values, etc.) in the running process of the motorcycle and converting them into digital signals for the calculation module.

[0008] The calibration module includes an oil level sensor calibration unit and an IMU (Inertial Measurement Unit) calibration unit; the oil level sensor calibration unit is used for calibrating the relationship between the resistance value of the oil level sensor and the remaining oil amount in the mailbox; and the IMU calibration unit is used for calibrating the posture of the IMU sensor relative to the motorcycle coordinate system.

[0009] The calculation module includes a motorcycle posture calculation unit, a remaining oil amount calculation unit and a range calculation unit; the motorcycle posture calculation unit is used for calculating the posture in the running process of the motorcycle and judging the motion state of the motorcycle; the remaining oil amount calculation unit is used for calculating the remaining fuel; and the range calculation unit is used for identifying the driving condition of the motorcycle and evaluating the range.

[0010] The human-computer interaction module includes a driving information display unit and a key input unit; the driving information display unit is used for displaying the driving information on the screen; and the key input unit is used for the driver to set the preference of the display content.

[0011] The second aspect of the present application discloses a motorcycle remaining fuel detection and range evaluation method, and the method comprises the following steps:

[0012] S1: Calibrate the IMU and the motorcycle fuel tank. The motorcycle is placed vertically on the ground, and the device is installed on the motorcycle. The accelerometer data of the IMU is used to calculate the roll installation angle and the pitch installation angle between the inertial navigation device and the vehicle body, and the heading angle is set as 0 by default. The conversion matrix between the inertial navigation device and the vehicle body is calculated. The correspondence between the real oil amount and the resistance value of the oil level sensor under the motorcycle level and various inclination angles is calibrated, and thus the mapping relationship between the posture angle and the remaining fuel percentage is fitted.

[0013] S2: Divide and identify motorcycle driving conditions. The historical driving data of the motorcycle (including speed, rotation speed, mileage) are preprocessed, and after eliminating obviously abnormal data, the segments are divided at a certain time interval. Principal component analysis is performed on all segments, several parameters with high correlation with principal components are selected as feature parameters of FCM (Fuzzy C-means) algorithm, the clustering model is trained using the feature parameters, the clustering centers of several typical conditions are obtained, which are used for driving condition discrimination, and the average fuel consumption under each typical condition is calculated.

[0014] S3: Read IMU data, low-pass and high-pass filter the x, y, z axis acceleration and gyroscope data collected by the IMU in real time, and then use the complementary filtering algorithm to calculate the motorcycle attitude. Compared with the traditional complementary filtering algorithm, the present application designs an adaptive proportional coefficient in PI regulation, combined with the motorcycle motion data, to obtain the optimal attitude in various motion processes. Finally, according to the value of the fuel sensor and the vehicle body attitude calculated by the IMU, the remaining fuel measured by the fuel sensor is compensated.

[0015] S4: Obtain motorcycle speed, rotation speed, ECU instantaneous fuel consumption and other related information, and judge the motion state of the motorcycle combined with the vehicle body attitude calculated by the IMU. The motion state of the motorcycle is divided into ON gear, idle speed, uniform speed driving and sudden acceleration and deceleration, which is used for optimizing the calculation of remaining fuel and cruising range module and judging the auxiliary refueling event and falling detection event.

[0016] S5: After obtaining the remaining fuel information detected by the fuel level sensor and the injection amount information of the ECU, information fusion is performed through the Kalman filter. The Kalman filtering process includes filter initialization, one-step prediction and measurement update. The initialization process includes system initial state value, system noise covariance matrix, prior covariance matrix and sensor noise covariance matrix initialization. One-step prediction is obtained by modeling the motorcycle remaining fuel and fuel consumption, and the values of the fuel sensor and the ECU injection amount are used as observations in measurement update. Finally, more accurate remaining fuel information is obtained. In addition, add refueling event processing, when refueling event occurs, Kalman filter parameters also need to be updated in time, in the case of ensuring the smoothness and accuracy of the remaining fuel measurement, improve the sensitivity of the whole system.

[0017] S6: Accumulate the fuel injection amount of the ECU in the recent mileage during the motorcycle running process, and calculate the average fuel consumption of the motorcycle in the recent period. Further judge the running condition of the motorcycle, first obtain the characteristic parameters of the electric vehicle in a certain running condition segment through calculation, calculate the distance from the to-be-identified condition segment to the clustering center of each type of running condition, and judge which type of running condition the to-be-identified segment belongs to according to the principle of minimum distance. Evaluate the actual average fuel consumption in combination with the known fuel consumption information under each type of condition, so as to adjust the predicted future average fuel consumption.

[0018] S7: Calculate the cruising range value by using the calculated remaining fuel amount and the predicted future average fuel consumption. Finally, display the remaining fuel amount and the cruising range value according to the demand.

[0019] Steps S1) - S7) are executed, so as to realize the motorcycle remaining fuel amount detection and the cruising range evaluation.

[0020] A third aspect of the present application relates to a motorcycle remaining fuel amount measurement and cruising range evaluation device, comprising a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to realize the motorcycle remaining fuel amount measurement and cruising range evaluation method of the present application.

[0021] A fourth aspect of the present application relates to a computer readable storage medium, which stores a program, and the program is executed by a processor to realize the motorcycle remaining fuel amount measurement and cruising range evaluation method of the present application.

[0022] The beneficial effects of the present application mainly include: a motorcycle remaining fuel amount and cruising range calculation method and device are provided. Compared with the traditional remaining fuel amount detection method, the present application uses the IMU to detect the real-time attitude, and then compensates the measurement value of the oil level sensor, and simultaneously combines the fuel injection amount information of the ECU to perform fusion calculation to obtain more accurate remaining fuel information. On this basis, combined with the running condition of the motorcycle, the cruising range information of various driving habits can be adaptively provided, and a more comfortable driving experience can be provided. The present application significantly improves the accuracy of the motorcycle remaining fuel amount and cruising range calculation, and has great reference significance for the trip arrangement of the driver. BRIEF DESCRIPTION OF DRAWINGS

[0023] The present application will be further explained below in combination with the drawings and specific implementation examples, and does not constitute a limitation on the present application.

[0024] FIG. 1 is a schematic diagram of a motorcycle remaining fuel amount and cruising range evaluation system provided by an embodiment of the present application; Figure 1 FIG. 1 is a schematic diagram of a motorcycle remaining fuel amount and cruising range evaluation system provided by an embodiment of the present application;

[0025] FIG. 1 is a schematic diagram of a motorcycle remaining fuel amount and cruising range evaluation system provided by an embodiment of the present application; Figure 2The flow chart of the motorcycle residual fuel quantity and cruising range evaluation method provided in Embodiment Two of the present application;

[0026] The Figure 3 The relationship between the posture angle and the residual fuel quantity fitted for the fuel sensor 210Ω;

[0027] The Figure 4 The residual fuel quantity change calculated by the present application during the real vehicle test process. DETAILED DESCRIPTION

[0028] In order to make the technical solution of the present application clearer and more complete, the technical solution provided by the present application will be described in detail below in combination with specific embodiments, and the present application will be further described in combination with the drawings. In particular, the drawings and the enumerated embodiments provided herein are only part of the present application and are used to explain the present application, but not to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0029] Embodiment One

[0030] Please refer to the Figure 1 The system framework diagram of the motorcycle residual fuel quantity detection and cruising range calculation system provided in Embodiment One of the present application. The motorcycle residual fuel quantity detection and cruising range evaluation system comprises a collection module, a calibration module, a calculation module and a man-machine interaction module.

[0031] The data collection module is used to collect various types of sensor signals (including pulse signals, analog signals, switching values, etc.) in the motorcycle running process and convert them into digital signals for use by the calculation module. In addition, since the information of the engine ECU (Engine Control Unit) needs to be used, communication needs to be established between the data collection module and the engine ECU.

[0032] The calibration module comprises an oil level sensor calibration unit and an IMU (Inertial Measurement Unit) calibration unit; the oil level sensor calibration unit is used to calibrate the relationship between the resistance value of the oil level sensor and the residual fuel quantity in the mailbox; and the IMU calibration unit is used to calibrate the posture of the IMU sensor relative to the motorcycle coordinate system.

[0033] The calculation module comprises a motorcycle posture calculation unit, a residual fuel quantity calculation unit and a cruising range calculation unit; the motorcycle posture calculation unit is used to calculate the posture in the motorcycle running process and judge the motion state of the motorcycle; the residual fuel quantity calculation unit is used to calculate the residual fuel quantity; and the cruising range calculation unit is used to identify the driving cycle of the motorcycle and evaluate the cruising range.

[0034] The human-computer interaction module comprises a driving information display unit and a key input unit; the driving information display unit is used for displaying driving information on a screen; and the key input unit is used for the driver to set preferences for the display content.

[0035] Optionally, in the motorcycle residual fuel detection and range calculation method, which will be described in detail in Example Two.

[0036] Example Two

[0037] Please refer to the attached Figure 2 The motorcycle residual fuel detection and range calculation method provided by the present application for Example Two comprises the following steps:

[0038] S1: Calibrate the IMU and the motorcycle fuel tank. Place the motorcycle vertically on the ground, install the device on the motorcycle. Calculate the roll installation angle and pitch installation angle between the inertial navigation device and the vehicle body using the accelerometer data of the IMU, and the heading angle is set to 0 by default. Calculate the conversion matrix between the inertial navigation device and the vehicle body. Calibrate the correspondence between the real fuel quantity and the resistance value of the fuel level sensor at different inclinations, and thus fit the mapping relationship between the attitude angle and the residual fuel percentage.

[0039] Further, in this example, the test motorcycle fuel tank has a capacity of 14L, the fuel sensor is a float-type resistance sensor, which is divided into 12 different resistance values, and the resistance value corresponds to the fuel quantity. The fuel test can be performed. The mapping relationship between the attitude angle and the residual fuel percentage needs to be calculated by fitting the data. In this example, take the resistance value of 210Ω (residual fuel quantity is 54.14%) as an example, in order to improve the compatibility with different motorcycles, the residual fuel quantity is expressed in percentage. The mapping relationship is shown in the attached Figure 3 .

[0040] S2: Divide the motorcycle driving conditions. Perform principal component analysis on the historical driving data of the motorcycle, select 3 parameters (average speed, maximum speed, and parking frequency) with high correlation with the principal components, and calculate the clustering centers of the 3 typical conditions as the basis for condition division. At the same time, calculate the average fuel consumption under each typical condition.

[0041] Specifically, S2 comprises the following steps:

[0042] S2-1: Preprocess the historical driving data of the motorcycle (including speed, rotation speed, and mileage), remove obviously abnormal data, and divide the data into segments at a certain time interval.

[0043] S2-2: Principal component analysis is performed on all the segments, and several parameters with greater correlation with principal components are selected as feature parameters of the clustering algorithm.

[0044] S2-3: FCM algorithm is used to cluster the formal data segments, which are divided into three typical working conditions. The specific steps include:

[0045] S2-3-1: The clustering algorithm related parameters are initialized, including the number of clusters c (set according to specific conditions), membership matrix U, cluster center V, fuzzy factor m, and iteration stopping condition. Assume that the historical data set X = {x1, x2, …, xN}. FCM can divide the data set into c (c = 3) classes, and the c cluster centers are [v1, v2, …, vC] respectively. n c

[0046] S2-3-2: Cluster center calculation: Where u ik is the membership of sample point x k to cluster center v i , and m is the fuzzy index (m > 1).

[0047] S2-3-3: The membership of each sample to each cluster center is: Where d ik is the distance between the sample point and the cluster center, the sample point x k and the cluster center v i , the Euclidean distance d ik = ||x k -v i || 2 .

[0048] S2-3-4: The final clustering result can be obtained through iteration steps S2-3-2 and S2-3-3, and the iteration termination condition is max (u (t) - u (t-1)) < ε or the iteration step number reaches the designed maximum iteration step number T.

[0049] S2-4: Calculate the average fuel consumption under each typical working condition.

[0050] S3: Read the IMU data, and process the x, y, z axis acceleration and gyroscope data collected by the IMU in real time to calculate the current attitude of the motorcycle. According to the value of the fuel sensor and the vehicle body attitude calculated by the IMU, the remaining fuel measured by the fuel sensor is compensated.

[0051] Specifically, the S3 includes the following steps:

[0052] ​​S3-1: Process the x, y, and z-axis acceleration and gyroscope data acquired in real time by the IMU. The acceleration data undergoes low-pass filtering, and the gyroscope data undergoes high-pass filtering. Normalization is then performed.

[0053]

[0054] S3-2: Transform the gravity from the world coordinate system to the vehicle coordinate system, normalize the gravity vector obtained from the accelerometer, and cross-multiply it with the gravity vector of the extracted attitude matrix to obtain the attitude error e(ω).

[0055]

[0056] S3-3: Use the attitude error e(ω) as the error value of the PI controller to control the drift of the IMU.

[0057]

[0058] Where K i K is the integral coefficient. p The proportional gain that is adaptively adjusted based on the motorcycle speed variance: Where K p_min It is K p The minimum value set for rapid acceleration and deceleration of a motorcycle, v var It represents the variance of motorcycle speed over a recent period.

[0059] S3-4: Add the compensation value e(ω) obtained earlier to the data output by the gyroscope for correction, and calculate the real-time attitude of the motorcycle.

[0060] S3-5: Calculate the compensated remaining fuel information based on the fuel sensor information, motorcycle posture, and the mapping relationship between the posture angle and remaining fuel percentage fitted in step S1.

[0061] S4: Analyzes CAN signals to obtain information such as motorcycle speed, RPM, and ECU instantaneous fuel consumption, and combines this with the vehicle's posture calculated by the IMU to determine the motorcycle's motion state. The motorcycle's motion state is divided into four states: ON gear, idling, constant speed driving, and rapid acceleration / deceleration. This is used to optimize the calculation of remaining fuel and range modules, as well as to assist in the judgment of events such as refueling and fall detection.

[0062] S5: After obtaining the fuel injection quantity information from the fuel level sensor and ECU, the information is fused through a Kalman filter. The Kalman filtering process includes filter initialization, one-step prediction, and measurement update.

[0063] Specifically, S5 includes the following steps:

[0064] S5-1: Kalman filter initialization process includes system initial state value, system noise covariance matrix, prior covariance matrix and sensor noise covariance matrix initialization.

[0065] Take the state vector X as a two-dimensional vector: X = [fδ] T . Where f is the value of the fuel sensor collected when the motorcycle starts, and δ is the engine fuel injection amount, which is the real-time fuel injection amount of the ECU here.

[0066] Noise covariance matrix: Q = diag([0.01 0.001] 2 ).

[0067] Prior covariance matrix: P = diag([1 1]

[0068] The sensor noise covariance matrix is set according to the sensor characteristics: R = diag([f var δ var ]) where f var is the variance of the oil level sensor measurement, and δ var is the fuel injection amount variance.

[0069] S5-2: Establish a remaining fuel model using fuel sensor information and ECU information, and perform one-step prediction of Kalman filter.

[0070] The state transition matrix is:

[0071] S5-3: Kalman filter measurement update, including observation matrix and observation:

[0072] The observation matrix H converts the measurement value to the corresponding state variable: H = diag([1 1]).

[0073] The observation Z is a two-dimensional column vector, Z = [f z δ z ], where f z is the measurement value of the fuel sensor. Due to vehicle vibration, road bumps and other reasons, the data needs to be filtered. The calculation method is: where f i represents the oil level percentage, σ i represents the number of times this value appears, and f represents the calculated result.

[0074] S6: Accumulate the ECU fuel injection amount of the last distance during motorcycle driving, and calculate the recent average fuel consumption of the motorcycle. Update the predicted future average fuel consumption combined with the driving conditions.

[0075] Specifically, the S6 includes the following steps:

[0076] S6-1: Accumulate the engine fuel consumption of the last distance, and calculate the average fuel consumption.

[0077] S6-2: Determine the driving condition of the motorcycle: obtain the characteristic parameters of the electric vehicle in a certain driving condition segment by calculation, calculate the distance from the to-be-identified condition segment to the clustering center of each type of driving condition, and determine which type of driving condition the to-be-identified segment belongs to according to the principle of minimum distance. The distance calculation formula is: wherein d i is the distance from the to-be-identified condition segment to the clustering center of the i-th type of driving condition; x k is the k-th characteristic parameter value in the three characteristic parameters of the to-be-identified segment; c ik is the clustering center of the k-th characteristic parameter under the i-th type of driving condition.

[0078] S6-3: Evaluate the actual average fuel consumption in combination with the known fuel consumption information under each type of condition to adjust the predicted future average fuel consumption.

[0079] S7: Calculate the cruising range value by using the calculated remaining fuel amount and the predicted future average fuel consumption. Finally, display the remaining fuel amount and the cruising range value according to the demand.

[0080] Embodiment Three

[0081] This embodiment relates to a motorcycle remaining fuel amount measurement and cruising range evaluation device, comprising a memory and one or more processors, the memory stores executable code, and the one or more processors execute the executable code to implement the motorcycle remaining fuel amount measurement and cruising range evaluation method of embodiment 1.

[0082] Embodiment Four

[0083] This embodiment relates to a computer readable storage medium, which stores a program, and the program is executed by a processor to implement the motorcycle remaining fuel amount measurement and cruising range evaluation method of embodiment 1.

[0084] Finally, the above is only the preferred embodiment of the present application and the technical principle applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for measuring the remaining fuel quantity and evaluating the cruising range of a motorcycle, characterized in that: The method comprises the following steps: S1: calibrate the IMU and the motorcycle fuel tank; the motorcycle is placed vertically on the ground, the conversion matrix between the inertial navigation device system and the vehicle system is calculated by using the accelerometer data of the IMU; the correspondence between the motorcycle horizontal and various inclination angles and the real fuel quantity and the resistance value of the fuel level sensor is calibrated, and thus the mapping relationship between the attitude angle and the remaining fuel percentage is fitted; S2: divide and identify the motorcycle driving conditions; the historical driving data of the motorcycle is subjected to principal component analysis, and the average vehicle speed, the maximum vehicle speed and the parking frequency are selected as the characteristic parameters of the fuzzy C-means clustering algorithm, the clustering center is calculated as the basis for dividing the conditions, and the average fuel consumption under each typical condition is calculated; S3: read the IMU data, the x, y and z axis acceleration and gyroscope data collected by the IMU in real time are subjected to low-pass and high-pass filtering respectively, and then the complementary filtering algorithm is used to calculate the motorcycle attitude; an adaptive proportional coefficient is designed in the PI regulation, the optimal attitude in various motion processes is obtained in combination with the motorcycle motion data; finally, the remaining fuel measured by the fuel sensor is compensated according to the value of the fuel sensor and the vehicle body attitude calculated by the IMU; S4: obtain the motorcycle speed, the rotating speed and the ECU instantaneous fuel consumption related information, and judge the motion state of the motorcycle in combination with the vehicle body attitude calculated by the IMU; the motion state of the motorcycle is divided into four states of ON, idle speed, uniform speed driving and sudden acceleration and deceleration, which are used for optimizing the calculation of the remaining fuel quantity and the cruising range and judging the auxiliary refueling event and the falling detection event; S5: fuse the fuel sensor and the instantaneous fuel consumption information based on the Kalman filtering algorithm, and obtain more accurate remaining fuel; S6: accumulate the fuel injection quantity of the ECU in the recent mileage during the driving of the motorcycle, calculate the recent average fuel consumption of the motorcycle, and update the predicted future average fuel consumption in combination with the driving condition; S7: calculate the cruising range value by using the calculated remaining fuel quantity and the predicted future average fuel consumption; finally, display the remaining fuel quantity and the cruising range value according to the requirement.

2. The method of measuring the remaining fuel quantity and evaluating the cruising range of a motorcycle according to claim 1, characterized in that: The calibration of the IMU in step S1 calculates the roll installation angle and the pitch installation angle between the inertial navigation device and the vehicle body by using the accelerometer data of the IMU, and the heading angle is defaulted as 0; the conversion matrix between the inertial navigation device system and the vehicle system is calculated; the calibration of the motorcycle fuel tank firstly determines the remaining fuel quantity corresponding to each resistance value of the fuel level sensor under the normal attitude of the motorcycle; secondly, the resistance value change of the fuel level sensor caused by the attitude change of the motorcycle under different fuel quantities is collected; finally, the mapping relationship between the attitude angle and the remaining fuel percentage is obtained by analyzing the data.

3. The method of measuring the remaining fuel quantity and evaluating the cruising range of a motorcycle according to claim 1, characterized in that: The driving condition identification based on the fuzzy C-means FCM in step S2 specifically comprises: pre-processing the historical driving data of the motorcycle, eliminating obviously abnormal data, and dividing the data into segments at a certain time interval; performing principal component analysis on all the segments, and selecting the average vehicle speed, the maximum vehicle speed and the parking frequency as the characteristic parameters of the clustering algorithm; training the clustering model by using the characteristic parameters, obtaining the clustering centers of several typical conditions, and using the clustering centers for subsequent discrimination of the cruising range driving condition.

4. The method of measuring the remaining oil quantity and evaluating the cruising range of a motorcycle according to claim 1, characterized in that: The motorcycle attitude estimation in step S3 uses a complementary filter algorithm, the proportional coefficient in the PI regulation An adaptive parameter is used to obtain the optimal attitude in various motion processes: wherein is The minimum value set in the case of motorcycle sudden acceleration and deceleration, is the variance of the motorcycle speed in the recent period of time.

5. The method of measuring the remaining fuel quantity and evaluating the cruising range of a motorcycle according to claim 1, characterized in that: The Kalman filter algorithm fusion fuel sensor and instantaneous fuel consumption information in step S5 includes: after obtaining the oil level sensor and the ECU injection amount information, information fusion is carried out through the Kalman filter, and the Kalman filter process includes filter initialization, one-step prediction and measurement update; Specifically, the S5 comprises the following steps: S5-1: the Kalman filter initialization process includes system initial state value, system noise covariance matrix, prior covariance matrix and sensor noise covariance matrix initialization; Taking the state vector X as a two-dimensional vector: ; wherein, is the value of the fuel sensor collected when the motorcycle is started, is the engine fuel injection amount, here taking the real-time fuel injection amount of the ECU; Noise covariance matrix: ; Prior covariance matrix: The sensor noise covariance matrix is set according to sensor characteristics as: wherein is the variance of the oil level sensor measurement, is the fuel injection quantity variance; S5-2: a remaining fuel model is established by using fuel sensor information and ECU information, and Kalman filter one-step prediction is carried out; The state transition matrix is: ; S5-3: Kalman filter measurement update, including observation matrix and observation: The observation matrix H converts the measurements to correspond to the state variables: ; The observation quantity Z is a two-dimensional column vector, wherein is the measured value of the fuel sensor; the data needs to be filtered due to vehicle vibration and road bumps; the calculation method is: ; wherein represents the hundredth ratio of the oil level, represents the number of times this value appears, represents the calculated result.

6. The method of measuring the remaining oil quantity and evaluating the cruising range of a motorcycle according to claim 1, characterized in that: Step S6 specifically includes: accumulating engine fuel consumption over the most recent distance and calculating the average fuel consumption; further determining the motorcycle's driving conditions, and evaluating the actual average fuel consumption based on known fuel consumption information under various driving conditions to adjust the predicted future average fuel consumption; driving condition identification uses the FCM cluster center calculation obtained in step S2: first, the feature parameters of the electric vehicle in a certain driving condition segment are obtained through calculation; then, the distance from the driving condition segment to be identified to each cluster center of various driving conditions is calculated; based on the principle of minimizing the distance, the type of driving condition to be identified is determined; the distance calculation formula is: ;in, The third of the three feature parameters of the segment to be identified Each characteristic parameter value; For belonging to the first The first type of driving condition Cluster centers of each feature parameter.

7. A device for measuring the remaining fuel quantity and evaluating the cruising range of a motorcycle, characterized in that, The memory stores executable code, and the one or more processors execute the executable code to implement the motorcycle remaining fuel measurement and range evaluation method in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, A program is stored thereon, which is executed by a processor to implement the motorcycle remaining fuel measurement and range evaluation method in any one of claims 1-6.

9. A motorcycle remaining fuel detection and range estimation system for implementing the motorcycle remaining fuel measurement and range estimation method as claimed in claim 1, characterized in that, Comprise: The acquisition module, the calibration module, the calculation module and the man-machine interaction module; The acquisition module is used for acquiring various sensor signals in the motorcycle running process, including pulse signals, analog signals, switching values, and converting them into digital signals for the calculation module; in addition, since the information of the engine ECU (Engine Control Unit, engine control unit) is needed, communication needs to be established between the acquisition module and the engine ECU; The calibration module includes an oil level sensor calibration unit and an IMU (Inertial Measurement Unit, inertial measurement unit) calibration unit; the oil level sensor calibration unit is used to calibrate the relationship between the resistance value of the oil level sensor and the remaining fuel in the mailbox; the IMU calibration unit is used to calibrate the attitude of the IMU sensor relative to the motorcycle coordinate system; The calculation module includes a motorcycle attitude calculation unit, a remaining fuel calculation unit and a range calculation unit; the motorcycle attitude calculation unit is used to calculate the attitude of the motorcycle in the running process and judge the motion state of the motorcycle; The remaining fuel calculation unit is used to calculate the remaining fuel; the range calculation unit is used for driving cycle recognition and range evaluation of the motorcycle; The man-machine interaction module includes a driving information display unit and a key input unit; The driving information display unit is used to display driving information on the screen; the key input unit is used for the driver to set the preference of the display content.

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