Method for forecasting running load of hybrid electric vehicle
A technology for hybrid vehicles and driving loads, applied to hybrid vehicles, motor vehicles, instruments, etc., can solve problems such as large errors, large calculations, and low precision, and achieve prediction accuracy and generalization capabilities and reduce complexity , accurate results
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[0118] A) Data sampling in the model building phase
[0119] The speed sequence x used by the speed sequence used in step S1 in this embodiment 0 ′(n) is generated from standard driving datasets in the real world, such as US06 Supplemental Federal TestProcedure (US06), New European Driving Cycle (NEDC), ManhattanBus Cycle (Manhattan), Highway Fuel Economy Test (HWFET), Urban Dynamometer Driving Schedule (UDDS), City Driving for a Heavy Vehicle (WVUCITY), Interstate Driving for a Heavy Vehicle (WVUINTER) and New York City Cycle (NYCC). Each data set is a time series collected by the sensors on the car, and each point in the time series represents the current instantaneous speed of the car.
[0120] These driving data can be divided into two categories: driving data of an urban environment and driving data of an out-of-urban environment. In urban environments, due to traffic control and congestion, driving data, such as Manhattan, UDDS, WVCITY and NYCC ( Figure 2A ~ Figure 2...
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