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Longitudinal Vehicle Dynamics Modeling and Parameter Estimation for Plug-in Hybrid Electric Vehicle
Abstract:
System identification is an important aspect in model-based control design which is proven to be a cost-effective and time saving approach to improve the performance of hybrid electric vehicles (HEVs). This study focuses on modeling and parameter estimation of the longitudinal vehicle dynamics for Toyota Prius Plug-in Hybrid (PHEV) with power-split architecture. This model is needed to develop and evaluate various controllers, such as energy management system, adaptive cruise control, traction and driveline oscillation control. Particular emphasis is given to the driveline oscillations caused due to low damping present in PHEVs by incorporating flexibility in the half shaft and time lag in the tire model. Accurate and reliable vehicle dynamics parameters that control the vehicle motion are estimated by acquiring experimental data from longitudinal maneuvers of the PHEV equipped with a vehicle measurement system (VMS), global positioning system (GPS) and inertial measurement unit (IMU). The simulated model with estimated parameters is analyzed for longitudinal dynamics by comparing with experimental data from on-road testing.