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This paper focuses on clamping force control of electronic wedge brakes without additional sensors for cost-effectiveness and system simplicity. Brake-by-wire systems can be used for enhanced, safe braking of intelligent and environmentally friendly vehicles such as gas-electric hybrid and electric vehicles. For implementation of the electronic wedge brake, the clamping force should be controlled properly even though model uncertainty and parameter variations exist due to the environment or system characteristics changes, e.g., temperature variations, pad wear, and nonlinear friction. In this paper, the electronic wedge brake is modeled to include the wedge dynamics as well as the nonlinearities such as backlash and friction in mechanical connections and clearance between the brake disk and pad. An on-line status monitoring algorithm using the simplified mathematical models is designed to estimate the mechanical system parameters.

Electro-Mechanical Brake (EMB) systems can provide improved braking and stability functions such as ABS, EBD, TCS, ESC, BA, ACC, etc. For the implementation of the EMB systems, reliable and robust fault detection algorithm is required. In this study, a model-based fault detection algorithm is designed based on the analytical redundancy method in order to monitor possible faults in EMB systems. The performance of the proposed model-based fault detection algorithm is verified in simulations. The effectiveness of the proposed algorithm is demonstrated in various faulty cases.

Electro-hydraulic brake (EHB) provides improved braking and stability functions such as ABS, TCS, ESC, EBD, etc. It also removes complex mechanical parts for freedom of design, improves maintenance requirements and reduces unit weight. But, the EHB should be at least as reliable as the conventional hydraulic brake system. In this paper, the model-based fault diagnosis system is developed to monitor the brake status using the analytical redundancy method. The performance of the model-based fault diagnosis system is verified in simulations and demonstrates the effectiveness of the proposed system in various faulty cases.

Rollover is one of the significant life threatening factors in SUVs (Sports Utility Vehicles). By applying braking or steering, active roll stability controllers help prevent rollover accidents in SUVs. The performance of these controllers is very sensitive to vehicle inertial parameters such as vehicle mass and mass center height. In this paper, a unified estimation method for vehicle mass is proposed considering available driving conditions, where three estimation algorithms are developed based on longitudinal, lateral or vertical vehicle dynamics, respectively. The first algorithm is designed using the longitudinal vehicle dynamics and the recursive least square with the disturbance observer technique for longitudinal traveling case. The second algorithm is designed using the lateral vehicle dynamics where the lateral velocity is estimated with the kinematic vehicle model via the Kalman filter.