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Subsection Coordinated Control during Mode Transition for a Compound Power-Split System
Technical Paper
2019-01-1214
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The power-split transmission is considered as one of the major technologies for hybrid electric vehicles. It utilizes two electric motors/generators (MGs) and a power-split device (planetary gear sets) to make the speed of internal combustion engine (ICE) independent from the vehicle speed, and in that way enables the ICE to operate in a high-efficiency region under all driving cycles. In this study, a compound power-split hybrid system integrated with a two-planetary gear train is proposed. To suppress the vehicle jerk intensity and improve the driving comfort during the transition from EV (Electric Vehicle) mode to HEV (Hybrid Electric Vehicle) mode, a torque coordinated control strategy is derived. Based on the analysis of mode transition in different sections, mathematical models of each section are deduced, respectively. Then a model-based torque coordinated control method is used to solve out the target output torques of ICE, MGs and brakes in each mode transition phase. To reduce the impact of engine ripple torque during the engine start process, a fuzzy control and two degrees of freedom active damping control algorithms are designed. In addition, since it is difficult to precisely measure the actual torque of ICE, an estimated algorithm based on the actual torques of MGs and the torque constraint relations of the planetary gear train is derived. To validate the developed control algorithm, a compound power-split hybrid powertrain test bench based on the planetary gear train and similarity principle is built. Both the simulation and bench test results show that the proposed subsection coordinated control method effectively suppresses the fluctuation of the output shaft torque and vehicle jerk.
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Li, M., Zhao, Z., Jiang, L., and Tang, X., "Subsection Coordinated Control during Mode Transition for a Compound Power-Split System," SAE Technical Paper 2019-01-1214, 2019, https://doi.org/10.4271/2019-01-1214.Data Sets - Support Documents
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