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This paper presents an adaptation method of equivalent factor in equivalent consumption minimization strategy (ECMS) of fuel cell hybrid electric vehicle (FCHEV) using hilly road information. Instantaneous optimization approach such as ECMS is one of real-time controllers. Furthermore, it is widely accepted that ECMS achieves near-optimum results with the selection of the appropriate equivalent factor. However, a lack of hilly road information no longer guarantees near-optimum results as well as charge-sustaining of ECMS under hilly road conditions. In this paper, first, an optimal control problem is formulated to derive ECMS analytical solution based on simplified models. Then, we proposed updating method of equivalent factor based on sensitivity analysis. The proposed method tries to mimic the globally optimal equivalent factor trajectory extracted from dynamic programming solutions.

Vehicle stability control system is a new idea which can enhance the vehicle stability and handling in the emergency situation. This system requires of the yaw rate, side slip angle, and road friction in order to control the traction force and the braking force at the individual wheels. This paper proposes an observer for vehicle stability control system. This observer consisted of the state observer for vehicle motion identification and the road condition estimator for the identification of the road friction coefficient. The state observer uses 2 degree-of-freedom bicycle model with the Dugoff tire and estimates the system variables based on the Kalman filter. The road condition estimator uses the same vehicle model and identifies the tire-road friction based on the recursive least square method. Both estimators make use of each other information.

Wheelbase preview control system that uses state and input estimator to reconstruct state and preview information is proposed. Conventional preview control systems use Kalman-Bucy filters of augmented system, which is composed of dynamics of a vehicle and a road profile for estimation scheme. Use of road model makes control performance sensitive to model errors that are inevitable in real applications. Compared with the conventional preview control systems, the proposed control system adopts a state and input estimator to estimate state and road input simultaneously. The state and input estimator does not require a road model, which makes it not robust to road model errors. However, the state and input estimator is sensitive to measurement noises, since it uses inverse dynamics of a system to estimate unknown inputs.