Map-based linear estimation of drive cycle for hybrid electric vehicles

Applications of hybrid electric vehicles (HEVs) and plug-in electric vehicles (PEVs) in modern power grids are increasing due to the growing concerns about environmental issues and unpredictable fuel prices. However, detailed information on drivers' behaviors which is required for vehicle control and management is not widely available. This paper presents a map-based linear estimation approach to estimate the drive cycles of hybrid electric vehicles (HEVs). It is shown that knowing geological data of the vehicle, a linear estimation of drive cycle is possible. Detailed simulations are presented to investigate the accuracy of the linear estimation compared with the real drive cycles. Simulation results are presented and analyzed for the linear estimations of two typical drive cycles including the highway fuel economy test (HWFET) cycle and the New York City cycle (NYCC).