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Torque control strategy and optimization for fuel consumption and emission reduction in parallel hybrid electric vehicles

机译:并联混合动力电动汽车的扭矩控制策略和油耗减少的优化

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To reduce fuel consumption and exhaust emissions in hybrid electric vehicles (HEVs), it is important to develop a well-organized energy management system (EMS). This paper proposes a torque control strategy coupled with optimization for a parallel HEV. A torque control strategy is developed first. In particular, a function to control the driving condition, called the internal combustion engine (ICE) torque control function, is introduced. This function controls the driving conditions (electric motor (EM) driving, ICE driving, and ICE driving assisted by EM) for reducing fuel consumption and exhaust emissions. This function depends on several design variables that should be optimized. Numerical simulation of HEV using Matlab/Simulink is so computationally intensive that a sequential approximate optimization (SAO) using a radial basis function network (RBF) is adopted to determine the optimal values of these design variables. As the result, the optimal ICE torque control function is determined with a small number of simulation runs. In this paper, CO2 and NOx emissions are minimized simultaneously for reducing the fuel consumption and exhaust emission. Through numerical simulations using typical driving cycles, the trade-off between CO2 and NOx emissions is clarified and the validity of the proposed torque control strategy coupled with the proposed optimization is examined.
机译:为了减少混合动力汽车(HEV)的燃料消耗和废气排放,开发组织良好的能源管理系统(EMS)非常重要。本文提出了一种扭矩控制策略,并结合了并联混合动力汽车的优化。首先开发了扭矩控制策略。特别地,引入了控制驾驶状态的功能,称为内燃机(ICE)扭矩控制功能。此功能控制行驶条件(电动机(EM)驱动,ICE驱动和由EM辅助的ICE驱动),以减少燃油消耗和废气排放。此功能取决于应优化的几个设计变量。使用Matlab / Simulink进行HEV的数值模拟计算量很大,以至于采用使用径向基函数网络(RBF)的顺序近似优化(SAO)来确定这些设计变量的最佳值。结果,通过少量仿真运行即可确定最佳的ICE扭矩控制功能。在本文中,同时减少了CO2和NOx排放,以减少燃料消耗和废气排放。通过使用典型行驶周期的数值模拟,阐明了CO2和NOx排放之间的权衡,并检验了所提出的转矩控制策略和所提出的优化方法的有效性。

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