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Vehicle parameter estimation using nested RLS algorithm

Energy consumption of a vehicle depends on the nature of road surface, grade and vehicle parameters. Predictive control strategies that rely on this information can benefit significantly from the knowledge of these parameters. This paper proposes an online estimation strategy to simultaneously estimate the vehicle mass, road frictional coefficient and wind velocity for a Series-Parallel Hybrid vehicle. A P2 hybrid vehicle longitudinal model is developed and used along with a two stage recursive least squares algorithm to estimate the dynamic parameters. The estimation strategy uses inputs from the vehicle longitudinal accelerometer sensor for determining road grade along with other powertrain signals.