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Abstract:A near-optimal rule-based mode control (RBC) strategy was proposed for a target plug-in hybrid electric vehicle (PHEV) taking into account the drivetrain losses. Individual loss models were developed for drivetrain components including the gears, planetary gear (PG), bearings, and oil pump, based on experimental data and mathematical governing equations. Also, a loss model for the power electronic system was constructed, including loss from the motor-generator while rotating in the unloaded state. To evaluate the effect of the drivetrain losses on the operating mode control strategy, backward simulations were performed using dynamic programming (DP). DP selects the operating mode, which provides the highest efficiency for given driving conditions. It was found that the operating mode selection changes when drivetrain losses are included, depending on driving conditions. An operating mode schedule was developed with respect to the wheel power and vehicle speed, and based on the operating mode schedule, a RBC was obtained, which can be implemented in an on-line application. To evaluate the performance of the RBC, a forward simulator was constructed for the target PHEV. The simulation results show near-optimal performance of the RBC compared with dynamic-programming-based mode control in terms of the mode operation time and fuel economy. The RBC developed with drivetrain losses taken into account showed a 4%-5% improvement of the fuel economy over a similar RBC, which neglected the drivetrain losses.

In this paper, a shift control algorithm to improve the shift quality was proposed for an electric vehicle with a dry-type two-speed dual-clutch transmission. To analyse the shift characteristics of the target electric vehicle, dynamic models for the two-speed dual-clutch transmission and the drivetrain were developed. Based on the dynamic models, dynamic equations for the transient shift states were derived, and a shift performance simulator was constructed. From analysis of the transient shift state, it was found that the fluctuations in the driveshaft torque, which cause the shift quality to deteriorate, occurred as a result of the inertia torque. Based on the analytical results, a control algorithm was proposed using traction motor torque control as well as shift actuator stroke control. For traction motor control, a compensation torque was applied during the inertia phase. In that phase, actuator stroke control was performed by considering the torque margin and the kissing point during the torque phase instead of the existing map-based control. To evaluate the performance of the proposed control algorithm, a test bench for the target electric vehicle was developed. From the experimental results, it was found that the variations in the driveshaft torque and in the jerk were reduced by the proposed control algorithm, which thereby provides an improved shift quality.

In this study, a gear fork control algorithm for a dual-clutch transmission is proposed to improve the shift quality for the downshift from second gear to first gear during coast-down. First, to investigate the shift characteristics, a dual-clutch transmission shift performance simulator was developed including the gear fork system. Using the dual-clutch transmission simulator, the shift characteristics for the downshift from second gear to first gear were investigated during coast-down. From the simulations and the test results, it was found that vibrations occur in the speed of the output shaft owing the large change in the speed gradient of the input shaft at the moment of synchronization, resulting in a change in the longitudinal acceleration of the vehicle, which causes the shift quality to deteriorate. Based on the dynamic models of the gear fork system and the test results, a gear fork control algorithm is proposed which generates a constant cone torque to reduce the speed gradient of the input shaft. It was found from the simulations and the vehicle test results that the amplitudes of the vibrations in the speed of the output shaft and the peak-to-peak acceleration of the vehicle were reduced by the gear fork control algorithm proposed in this study, which improved the shift quality by as much as 50%.

Abstract:What is the best number of gear steps for parallel type hybrid electric vehicles (HEVs) and what are the pros and cons of the power split type HEV compared to the parallel type have been interesting issues in the development of HEVs. In this study, a comparative analysis was performed to evaluate the fuel economy potential of a parallel HEV and a power split type HEV. First, the fuel economy potential of the parallel HEV was investigated for the number of gear steps. Four-speed, six-speed, and eight-speed automatic transmissions (ATs) and a continuously variable transmission (CVT) were selected, and their drivetrain losses were considered in the dynamic programming (DP). It was found from DP results that the power electronics system (PE) loss decreased because the magnitude of the motor load leveling power decreased as the number of gear steps increased. On the other hand, the drivetrain losses including the electric oil pump (EOP) loss increased with increasing gear step. The improvement rate from the 4-speed to the 6-speed was the greatest, while it decreased for the higher gear step. The fuel economy of the CVT HEV was rather low due to the large EOP loss in spite of the reduced PE loss. In addition, the powertrain characteristics of the parallel HEV were compared with the power split type HEV. In the power split type HEV, the PE loss was almost double compared to that of the parallel HEV because two large capacity motor-generators were used. However, the drivetrain loss and EOP loss of the power split type HEV were found to be much smaller due to its relatively simple architecture. It is expected that the power characteristics of the parallel and power split type HEVs obtained from the DP results can be used in the development of HEV systems.