Method and system for providing services for range anxiety users on the basis of internet of vehicles
A vehicle networking and anxiety technology, applied in electric vehicles, vehicle energy storage, vehicle components, etc., can solve problems such as unpractical tools and lack of systematic planning for solutions, and achieve the effect of reducing anxiety.
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Embodiment 1
[0030] Embodiment one: see figure 1 , to provide services to users with mileage anxiety based on the Internet of Vehicles, which is realized by information interaction between the service system embedded in the Internet of Vehicles platform and the vehicle. The specific steps are as follows:
[0031] 101: Collect data
[0032] Collect battery status information, electric vehicle location information, electric vehicle speed information, electric vehicle output torque information, etc. through the data collection module.
[0033] 102: Calculate estimated driving distance
[0034] The estimated mileage calculation module calculates the estimated mileage when the electric vehicle runs at the current driving speed and output torque based on the collected above information, and calculates the expected mileage when the battery capacity drops to the preset capacity ratio. Usually, the preset capacity ratios for the battery capacity reduction are divided into at least 3, and the rati...
Embodiment 2
[0049] Embodiment 2: Evaluate the user's mileage anxiety degree and determine the user's anxiety level, including the following steps:
[0050] 1. Extract user charging behavior data from the Internet of Vehicles database.
[0051] The extracted data types include: SOC of electric vehicles before charging, SOC of electric vehicles after charging, single charging time, weekly charging frequency, continuous driving mileage at low SOC, nominal cruising range of electric vehicle manufacturers, etc.
[0052] 2. Extract the eigenvalues that reflect the user's mileage anxiety through a clustering algorithm.
[0053] The extracted user charging behavior data is used to classify users through a clustering algorithm. The classification is based on the user's mileage anxiety level. The classification method is as follows: preprocessing the extracted user charging behavior data, the preprocessing method is normalization, mainly dividing the continuous driving mileage at low SOC by the...
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