Method and device for on-line prediction of remaining driving mileage of electric vehicle
First Claim
1. A method for on-line prediction of remaining driving mileage of an electric vehicle, characterized in that, the method comprises using a device for on-line prediction of remaining driving mileage of an electric vehicle to execute the steps of:
- acquiring in-transit data of the electric vehicle which is driving through a CAN (Controller Area Network) bus, the in-transit data including battery status information, motor status information and vehicle speed information, wherein the battery status information includes the value of voltage of a battery pack, the value of current of the battery pack and the value of remaining charge of the battery pack, the motor status information includes a motor speed, and the vehicle speed information includes a current driving speed and driving mileage;
acquiring driving environment data of the electric vehicle through a driving environment data collecting device including a network device which can directly obtain corresponding weather information from the relevant authority and a measuring device which can obtain the road condition information, the driving environment data including weather information and road condition information, wherein the weather information includes the type of weather at the current geographical position and an ambient temperature, and the road condition information includes the type and tilt degree of road surfaces;
calculating power consumption per mileage of the electric vehicle in the current case by using the in-transit data and the driving environment data in combination with a updated power consumption rate data model; and
predicting the remaining driving mileage of the electric vehicle based on the power consumption per mileage;
wherein the updated power consumption rate data model is obtained through following steps;
acquiring the in-transit data of the electric vehicle to form a historical running data set, and acquiring the driving environment data of the electric vehicle to form a historical driving environment data set;
fusing the historical running data set and the historical driving environment data set to obtain in-transit time series data of the electric vehicle;
acquiring correlation between the power consumption per mileage of the electric vehicle and the respective parameter in the historical running data set and the historical driving environment data set by using a correlation analysis method in a preset driving environment; and
after removing parameters whose correlations are less than a preset value of correlation, fitting the power consumption per mileage and remaining parameters to obtain the power consumption rate data model of the electric vehicle.
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Abstract
The present invention relates to a method and device for on-line prediction of remaining driving mileage of an electric vehicle. The method comprises: acquiring in-transit data and driving environment data of the electric vehicle which is driving; calculating the power consumption per mileage of the electric vehicle in the current case by using the in-transit data and the driving environment data in combination with a power consumption rate data model; predicting the remaining driving mileage of the electric vehicle based on the power consumption per mileage. The device provided by the present invention is implemented on the basis of the method above. The prediction result of the present invention is more accurate, to avoid the problem that the power is exhausted due to exceeding the mileage expected by a user so that the electric vehicle cannot continue to drive, thereby improving the driving experience of the user.
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Citations
7 Claims
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1. A method for on-line prediction of remaining driving mileage of an electric vehicle, characterized in that, the method comprises using a device for on-line prediction of remaining driving mileage of an electric vehicle to execute the steps of:
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acquiring in-transit data of the electric vehicle which is driving through a CAN (Controller Area Network) bus, the in-transit data including battery status information, motor status information and vehicle speed information, wherein the battery status information includes the value of voltage of a battery pack, the value of current of the battery pack and the value of remaining charge of the battery pack, the motor status information includes a motor speed, and the vehicle speed information includes a current driving speed and driving mileage; acquiring driving environment data of the electric vehicle through a driving environment data collecting device including a network device which can directly obtain corresponding weather information from the relevant authority and a measuring device which can obtain the road condition information, the driving environment data including weather information and road condition information, wherein the weather information includes the type of weather at the current geographical position and an ambient temperature, and the road condition information includes the type and tilt degree of road surfaces; calculating power consumption per mileage of the electric vehicle in the current case by using the in-transit data and the driving environment data in combination with a updated power consumption rate data model; and predicting the remaining driving mileage of the electric vehicle based on the power consumption per mileage; wherein the updated power consumption rate data model is obtained through following steps; acquiring the in-transit data of the electric vehicle to form a historical running data set, and acquiring the driving environment data of the electric vehicle to form a historical driving environment data set; fusing the historical running data set and the historical driving environment data set to obtain in-transit time series data of the electric vehicle; acquiring correlation between the power consumption per mileage of the electric vehicle and the respective parameter in the historical running data set and the historical driving environment data set by using a correlation analysis method in a preset driving environment; and after removing parameters whose correlations are less than a preset value of correlation, fitting the power consumption per mileage and remaining parameters to obtain the power consumption rate data model of the electric vehicle. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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Specification