A vehicle stability enhancement control algorithm for a four-wheel-drive hybrid electric vehicle (HEV) is proposed using rear motor driving, regenerative braking control, and electrohydraulic brake (EHB) control. A fuzzy-rule-based control algorithm is proposed, which generates the direct yaw moment to compensate for the errors of the sideslip angle and yaw rate. Performance of the vehicle stability control algorithm is evaluated using ADAMS and MATLAB Simulink cosimulations. HEV chassis elements such as the tires, suspension system, and steering system are modeled to describe the vehicle's dynamic behavior in more detail using ADAMS, whereas HEV power train elements such as the engine, motor, battery, and transmission are modeled using MATLAB Simulink with the control algorithm. It is found from the simulation results that the driving and regenerative braking at the rear motor is able to provide improved stability. In addition, better performance can be achieved by applying the driving and regenerative braking control, as well as EHB control.

The estimation of the frictional and clamping forces is one of the key issues in the design and implementation of the electromechanical brake (EMB) system. In this paper, a clamping-force control algorithm is presented along with a consideration of the frictional characteristics and the estimation of the clamping force for a planetary reduction gear-type EMB. The frictional model is developed by including the frictional effect of the motor, the planetary gear, and the screw thread. Since the clamping-force estimation requires the distance of the contact gap between the pad and the disk, an initial gap-distance control algorithm that uses the gradient change of the motor torque is proposed. This paper is concluded with a discussion of the performance of the control algorithm by comparing the simulation results with the experimental results.

A mode control strategy for a two-mode hybrid electric vehicle (HEV) with a fixed-gear mode is proposed, considering wheel torque demand while minimizing fuel consumption. First, the dynamic equations of a power-split HEV are derived, and these equations consist of two electrically variable transmission modes and four fixed-gear modes. Using the dynamic equations, the optimal operation mode is selected from the viewpoint of the maximum available output torque with respect to the driver's demand and vehicle velocity. Next, an optimized mode selection map is constructed using optimization to minimize fuel consumption while satisfying the required wheel torque. We note that fixed-gear mode uses the fixed-gear mode 1 to implement a higher gear ratio if the vehicle speed is low and if the required output torque is large, as in a conventional automatic transmission shift map, and selects the fixed-gear mode 4 to implement a lower gear ratio if the vehicle speed is high and if the required output torque is low. A vehicle simulation was performed to verify the mode control algorithm of the two-mode HEV, and the results were compared with vehicle experimental results. The simulation results showed that the mode control strategy for the two-mode HEV gave satisfactory performance, similar to the experiment results.

A regenerative braking algorithm is proposed for a hybrid electric vehicle with a continuously variable transmission (CVT) to make the maximum use of the regenerative braking energy. In the regenerative braking algorithm, the regenerative torque is determined by considering the motor capacity, battery state of charge (SOC), and vehicle velocity. The regenerative braking force is calculated from the brake control unit by comparing the demanded brake torque and the motor torque available. The wheel pressure is reduced by the amount of the regenerative braking force and is supplied from the hydraulic brake module. In addition, the CVT speed ratio control algorithm is suggested during braking for optimal motor operation. The optimal operation line is proposed to operate the motor in the most efficient region while keeping the motor speed as low as possible by considering engine noise and friction. It is found from the experiments that the regenerative braking algorithm with CVT ratio control offers an improved battery SOC, which provides increased recuperation energy by 8 per cent for the federal urban driving schedule compared with that of the conventional CVT ratio control.